LMU BioDB 2019 - User contributions [en]

Week 8

2020-04-22T17:41:21Z

Kdahlquist: Undo revision 4860 by Imacarae (talk)undo the edits made by Ivy because she made them on the wrong pge

'''This journal entry is due on Thursday, October 24, at 12:01am Pacific time.'''

'''''Note that you have an interim deadline of Tuesday, October 22, at 12:01am Pacific time to upload your workbook with the ANOVA and Bonferroni and Benjamini-Hochberg corrections to the wiki, so that your calculations can be checked before proceeding with the rest of the protocol.'''''

== Objectives ==

The purpose of this assignment is:
* to conduct the "analyze" step of the data life cycle for a DNA microarray dataset.
* to develop an intuition about what different p-value cut-offs mean.
* to keep a detailed electronic laboratory notebook to facilitate reproducible research.

== Individual Journal Assignment ==

* Store this journal entry as "''username'' Week 8" (i.e., this is the text to place between the square brackets when you link to this page).
* '''''Invoke''''' your template on your journal entry page so that you:
** Link from your journal entry page to this Assignment page.
** Link from your journal entry to your user page.
** Add the "Journal Entry" category to the end of your wiki page.
* Because you have invoked your template on your user page, you should also have a:
** Link from your user page to this Assignment page.
** Link to your journal entry from your user page.
* The sections you need for this week are Purpose, Methods/Results (one combined section), Data & Files, Conclusion, Acknowledgments, and References (as specified by the [[Week_1 | Week 1]] assignment).
* For your assignment this week, the electronic laboratory notebook you will keep on your individual wiki page is crucial. An electronic laboratory notebook records all the manipulations you perform on the data and the answers to the questions throughout the protocol. Like a paper lab notebook found in a wet lab, it should contain enough information so that you or someone else could reproduce what you did using only the information from the notebook.
** We will be performing a series of computations on a microarray dataset, primarily using Microsoft Excel. In the interests of reproducible research, it is appropriate to copy and paste the methods from this assignment into your individual journal entry.
** You '''''must''''' then '''''modify''''' the general instructions (which are generic to the whole class) to your own data analysis, recording the specific modifications and equations that you used on '''your''' dataset.
** Record the answers to the questions posed in the protocol at the place in which they appear in the method. You do not need to separate them out in a different results section.
** All files generated in the protocol must be uploaded to the wiki and linked to from your journal entry page in a "Data and Files" section.
** You will write a summary paragraph that gives the conclusions from this week's analysis.

=== Homework Partners ===

You will work in groups of three or four for the next two weeks of assignments. You will keep the same group for the next two weeks. Please sit next to your group members in class. You will be expected to consult with your group members, in order to complete the assignment. However, unless otherwise stated, each partner must submit his or her own work as the individual journal entry (direct copies of each other's work is not allowed). Homework partners for this week are:

* Marcus & Jonar, Christina & Kaitlyn ([[Media:BIOL367_F19_microarray-data_wt.xlsx | wild type data]])
* Ivy & Emma, DeLisa & Mihir ([[Media:BIOL367_F19_microarray-data_dCIN5.xlsx | ''Δcin5'' data]])
* Iliana, Mike, Joey ([[Media:BIOL367_F19_microarray-data_dGLN3.xlsx | ''Δgln3'' data]])
* Aby, David, Naomi ([[Media:BIOL367_F19_microarray-data_dHAP4.xlsx | ''Δhap4'' data]])

=== Microarray Data Analysis ===

We will be working on the protocols in class on Thursday, October 17 and Tuesday, October 22. Based on the progress that is made during class, the milestone of what needs to be completed by the Week 8 journal deadline will be announced in class. We will continue with this protocol for the Week 9 assignment.

==== Background ====

This is a list of steps required to analyze DNA microarray data.

#Quantitate the fluorescence signal in each spot
#Calculate the ratio of red/green fluorescence
#Log<sub>2</sub> transform the ratios
#* Steps 1-3 have been performed for you by the GenePix Pro software (which runs the microarray scanner).
#Normalize the ratios on each microarray slide
#Normalize the ratios for a set of slides in an experiment
#* Steps 4-5 was performed for you using a script in R, a statistics package (see: [https://openwetware.org/wiki/Dahlquist:Microarray_Data_Analysis_Workflow#Steps_4-5:_Within-_and_Between-chip_Normalization Microarray Data Analysis Workflow])
#* You will perform the following steps:
#Perform statistical analysis on the ratios
#Compare individual genes with known data
#* Steps 6-7 are performed in Microsoft Excel
#Pattern finding algorithms (clustering)
#Map onto biological pathways
#* We will use software called STEM for the clustering and mapping
#* [https://academic.oup.com/bioinformatics/article/21/suppl_1/i159/203147 Ernst, J., & Bar-Joseph, Z. (2006). STEM: a tool for the analysis of short time series gene expression data. ''BMC bioinformatics'', ''7''(1), 191. DOI: 10.1093/bioinformatics/bti1022]
# Identifying regulatory transcription factors responsible for observed changes in gene expression (YEASTRACT)
#* [https://academic.oup.com/nar/article/46/D1/D348/4191333 Teixeira, M. C., Monteiro, P. T., Palma, M., Costa, C., Godinho, C. P., Pais, P., ... & Sá-Correia, I. (2017). YEASTRACT: an upgraded database for the analysis of transcription regulatory networks in ''Saccharomyces cerevisiae''. ''Nucleic acids research'', ''46''(D1), D348-D353.]
# Dynamical systems modeling of the gene regulatory network ([http://kdahlquist.github.io/GRNmap/ GRNmap])
#* [https://link.springer.com/article/10.1007/s11538-015-0092-6 Dahlquist, K. D., Fitzpatrick, B. G., Camacho, E. T., Entzminger, S. D., & Wanner, N. C. (2015). Parameter estimation for gene regulatory networks from microarray data: cold shock response in ''Saccharomyces cerevisiae''. ''Bulletin of mathematical biology'', ''77''(8), 1457-1492. DOI: 10.1007/s11538-015-0092-6]
# Viewing modeling results in [http://dondi.github.io/GRNsight/ GRNsight]
#* [https://peerj.com/articles/cs-85/ Dahlquist, K. D., Dionisio, J. D. N., Fitzpatrick, B. G., Anguiano, N. A., Varshneya, A., Southwick, B. J., & Samdarshi, M. (2016). GRNsight: a web application and service for visualizing models of small-to medium-scale gene regulatory networks. ''PeerJ Computer Science'', ''2'', e85. DOI: 10.7717/peerj-cs.85]

==== Experimental Design and Getting Ready ====

The data used in this exercise is publicly available at the NCBI GEO database in [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE83656 record GSE83656].
* Begin by downloading the Excel file for your group's strain.
** Marcus & Jonar, Christina & Kaitlyn ([[Media:BIOL367_F19_microarray-data_wt.xlsx | wild type data]])
** Ivy & Emma, DeLisa & Mihir ([[Media:BIOL367_F19_microarray-data_dCIN5.xlsx | ''Δcin5'' data]])
** Iliana, Mike, Joey ([[Media:BIOL367_F19_microarray-data_dGLN3.xlsx | ''Δgln3'' data]])
** Aby, David, Naomi ([[Media:BIOL367_F19_microarray-data_dHAP4.xlsx | ''Δhap4'' data]])
* '''NOTE: before beginning any analysis, immediately change the filename (Save As...) so that it contains your initials to distinguish it from other students' work.'''
* In the Excel spreadsheet, there is a worksheet labeled "Master_Sheet_<STRAIN>", where <STRAIN> is replaced by the strain designation, wt, dCIN5, dGLN3, or dHAP4.
** In this worksheet, each row contains the data for one gene (one spot on the microarray).
** The first column contains the "MasterIndex", which numbers all of the rows sequentially in the worksheet so that we can always use it to sort the genes into the order they were in when we started.
** The second column (labeled "ID") contains the Systematic Name (gene identifier) from the [http://www.yeastgenome.org Saccharomyces Genome Database].
** The third column contains the Standard Name for each of the genes.
** Each subsequent column contains the log<sub>2</sub> ratio of the red/green fluorescence from each microarray hybridized in the experiment (steps 1-5 above having been performed for you already), for each strain starting with wild type and proceeding in alphabetical order by strain deletion.
** Each of the column headings from the data begin with the experiment name ("wt" for wild type ''S. cerevisiae'' data, "dCIN5" for the ''Δcin5'' data, etc.). "LogFC" stands for "Log<sub>2</sub> Fold Change" which is the Log<sub>2</sub> red/green ratio. The timepoints are designated as "t" followed by a number in minutes. Replicates are numbered as "-0", "-1", "-2", etc. after the timepoint.
*** The timepoints are t15, t30, t60 (cold shock at 13°C) and t90 and t120 (cold shock at 13°C followed by 30 or 60 minutes of recovery at 30°C).
* '''''Begin by recording in your wiki, the strain that you will analyze, the filename, the number of replicates for each strain and each time point in your data.'''''

'''''Note that you should have already completed this section as part of the [[Week 7]] assignment. Please copy over the information recorded on your [[Week 7]] journal to your [[Week 8]] individual journal entry.'''''



==== Statistical Analysis Part 1: ANOVA ====

The purpose of the within-stain ANOVA test is to determine if any genes had a gene expression change that was significantly different than zero at '''''any''''' timepoint.

# Create a new worksheet, naming it either "(STRAIN)_ANOVA" as appropriate. For example, you might call yours "wt_ANOVA" or "dHAP4_ANOVA"
# Copy the first three columns containing the "MasterIndex", "ID", and "Standard Name" from the "Master_Sheet" worksheet for your strain and paste it into your new worksheet. Copy the columns containing the data for your strain and paste it into your new worksheet.
# At the top of the first column to the right of your data, create five column headers of the form (STRAIN)_AvgLogFC_(TIME) where STRAIN is your strain designation and (TIME) is 15, 30, etc.
# In the cell below the (STRAIN)_AvgLogFC_t15 header, type <code>=AVERAGE(</code>
# Then highlight all the data in row 2 associated with t15, press the closing paren key (shift 0),and press the "enter" key.
# This cell now contains the average of the log fold change data from the first gene at t=15 minutes.
# Click on this cell and position your cursor at the bottom right corner. You should see your cursor change to a thin black plus sign (not a chubby white one). When it does, double click, and the formula will magically be copied to the entire column of 6188 other genes.
# Repeat steps (4) through (8) with the t30, t60, t90, and the t120 data.
# Now in the first empty column to the right of the (STRAIN)_AvgLogFC_t120 calculation, create the column header (STRAIN)_ss_HO.
# In the first cell below this header, type <code>=SUMSQ(</code>
# Highlight all the LogFC data in row 2 (but not the AvgLogFC), press the closing paren key (shift 0),and press the "enter" key.
# In the next empty column to the right of (STRAIN)_ss_HO, create the column headers (STRAIN)_ss_(TIME) as in (3).
# Make a note of how many data points you have at each time point for your strain. For most of the strains, it will be 4, but for dHAP4 t90 or t120, it will be "3", and for the wild type it will be "4" or "5". Count carefully. Also, make a note of the total number of data points. Again, for most strains, this will be 20, but for example, dHAP4, this number will be 18, and for wt it should be 23 (double-check).
# In the first cell below the header (STRAIN)_ss_t15, type <code>=SUMSQ(<range of cells for logFC_t15>)-COUNTA(<range of cells for logFC_t15>)*<AvgLogFC_t15>^2</code> and hit enter.
#* The <code>COUNTA</code> function counts the number of cells in the specified range that have data in them (i.e., does not count cells with missing values).
#* The phrase <range of cells for logFC_t15> should be replaced by the data range associated with t15.
#* The phrase <AvgLogFC_t15> should be replaced by the cell number in which you computed the AvgLogFC for t15, and the "^2" squares that value.
#* Upon completion of this single computation, use the Step (7) trick to copy the formula throughout the column.
# Repeat this computation for the t30 through t120 data points. Again, be sure to get the data for each time point, type the right number of data points, and get the average from the appropriate cell for each time point, and copy the formula to the whole column for each computation.
# In the first column to the right of (STRAIN)_ss_t120, create the column header (STRAIN)_SS_full.
# In the first row below this header, type <code>=sum(<range of cells containing "ss" for each timepoint>)</code> and hit enter.
# In the next two columns to the right, create the headers (STRAIN)_Fstat and (STRAIN)_p-value.
# Recall the number of data points from (13): call that total n.
# In the first cell of the (STRAIN)_Fstat column, type <code>=((n-5)/5)*(<(STRAIN)_ss_HO>-<(STRAIN)_SS_full>)/<(STRAIN)_SS_full></code> and hit enter.
#* Don't actually type the n but instead use the number from (13). Also note that "5" is the number of timepoints.
#* Replace the phrase (STRAIN)_ss_HO with the cell designation.
#* Replace the phrase <(STRAIN)_SS_full> with the cell designation.
#* Copy to the whole column.
# In the first cell below the (STRAIN)_p-value header, type <code>=FDIST(<(STRAIN)_Fstat>,5,n-5)</code> replacing the phrase <(STRAIN)_Fstat> with the cell designation and the "n" as in (13) with the number of data points total. . Copy to the whole column.
# Before we move on to the next step, we will perform a quick sanity check to see if we did all of these computations correctly.
#* Click on cell A1 and click on the Data tab. Select the Filter icon (looks like a funnel). Little drop-down arrows should appear at the top of each column. This will enable us to filter the data according to criteria we set.
#* Click on the drop-down arrow on your (STRAIN)_p-value column. Select "Number Filters". In the window that appears, set a criterion that will filter your data so that the p value has to be less than 0.05.
#* Excel will now only display the rows that correspond to data meeting that filtering criterion. A number will appear in the lower left hand corner of the window giving you the number of rows that meet that criterion. We will check our results with each other to make sure that the computations were performed correctly.
#* Be sure to undo any filters that you have applied before making any additional calculations.

==== Calculate the Bonferroni and p value Correction ====

''Note: Be sure to undo any filters that you have applied before continuing with the next steps.''
# Now we will perform adjustments to the p value to correct for the [https://xkcd.com/882/ multiple testing problem]. Label the next two columns to the right with the same label, (STRAIN)_Bonferroni_p-value.
# Type the equation <code>=<(STRAIN)_p-value>*6189</code>, Upon completion of this single computation, use the Step (10) trick to copy the formula throughout the column.
# Replace any corrected p value that is greater than 1 by the number 1 by typing the following formula into the first cell below the second (STRAIN)_Bonferroni_p-value header: <code>=IF((STRAIN)_Bonferroni_p-value>1,1,(STRAIN)_Bonferroni_p-value)</code>, where "(STRAIN)_Bonferroni_p-value" refers to the cell in which the first Bonferroni p value computation was made. Use the Step (10) trick to copy the formula throughout the column.

==== Calculate the Benjamini & Hochberg p value Correction ====

# Insert a new worksheet named "(STRAIN)_ANOVA_B-H".
# Copy and paste the "MasterIndex", "ID", and "Standard Name" columns from your previous worksheet into the first two columns of the new worksheet.
# For the following, use Paste special > Paste values. Copy your unadjusted p values from your ANOVA worksheet and paste it into Column D.
# Select all of columns A, B, C, and D. Sort by ascending values on Column D. Click the sort button from A to Z on the toolbar, in the window that appears, sort by column D, smallest to largest.
# Type the header "Rank" in cell E1. We will create a series of numbers in ascending order from 1 to 6189 in this column. This is the p value rank, smallest to largest. Type "1" into cell E2 and "2" into cell E3. Select both cells E2 and E3. Double-click on the plus sign on the lower right-hand corner of your selection to fill the column with a series of numbers from 1 to 6189.
# Now you can calculate the Benjamini and Hochberg p value correction. Type (STRAIN)_B-H_p-value in cell F1. Type the following formula in cell F2: <code>=(D2*6189)/E2</code> and press enter. Copy that equation to the entire column.
# Type "STRAIN_B-H_p-value" into cell G1.
# Type the following formula into cell G2: <code>=IF(F2>1,1,F2)</code> and press enter. Copy that equation to the entire column.
# Select columns A through G. Now sort them by your MasterIndex in Column A in ascending order.
# Copy column G and use Paste special > Paste values to paste it into the next column on the right of your ANOVA sheet.

* '''''Zip and upload the .xlsx file that you have just created to the wiki.'''''

'''''You must finish up to this point for the interim deadline of Tuesday, October 22, 12:01am Pacific time, so that the instructor can check your calculations before class.'''''

==== Sanity Check: Number of genes significantly changed ====

Before we move on to further analysis of the data, we want to perform a more extensive sanity check to make sure that we performed our data analysis correctly. We are going to find out the number of genes that are significantly changed at various p value cut-offs.

* Go to your (STRAIN)_ANOVA worksheet.
* Select row 1 (the row with your column headers) and select the menu item Data > Filter > Autofilter (The funnel icon on the Data tab). Little drop-down arrows should appear at the top of each column. This will enable us to filter the data according to criteria we set.
* Click on the drop-down arrow for the unadjusted p value. Set a criterion that will filter your data so that the p value has to be less than 0.05.
** '''''How many genes have p < 0.05? and what is the percentage (out of 6189)?'''''
** '''''How many genes have p < 0.01? and what is the percentage (out of 6189)?'''''
** '''''How many genes have p < 0.001? and what is the percentage (out of 6189)?'''''
** '''''How many genes have p < 0.0001? and what is the percentage (out of 6189)?'''''
** Note that it is a good idea to create a new worksheet in your workbook to record the answers to these questions. Then you can write a formula in Excel to automatically calculate the percentage for you.
* When we use a p value cut-off of p < 0.05, what we are saying is that you would have seen a gene expression change that deviates this far from zero by chance less than 5% of the time.
* We have just performed 6189 hypothesis tests. Another way to state what we are seeing with p < 0.05 is that we would expect to see this a gene expression change for at least one of the timepoints by chance in about 5% of our tests, or 309 times. Since we have more than 309 genes that pass this cut off, we know that some genes are significantly changed. However, we don't know ''which'' ones. To apply a more stringent criterion to our p values, we performed the Bonferroni and Benjamini and Hochberg corrections to these unadjusted p values. The Bonferroni correction is very stringent. The Benjamini-Hochberg correction is less stringent. To see this relationship, filter your data to determine the following:
** '''''How many genes are p < 0.05 for the Bonferroni-corrected p value? and what is the percentage (out of 6189)?'''''
** '''''How many genes are p < 0.05 for the Benjamini and Hochberg-corrected p value? and what is the percentage (out of 6189)?'''''
* In summary, the p value cut-off should not be thought of as some magical number at which data becomes "significant". Instead, it is a moveable confidence level. If we want to be very confident of our data, use a small p value cut-off. If we are OK with being less confident about a gene expression change and want to include more genes in our analysis, we can use a larger p value cut-off.
* We will compare the numbers we get between the wild type strain and the other strains studied, organized as a table. Use this [[Media:BIOL367_F19_sample_p-value_slide.pptx | sample PowerPoint slide]] to see how your table should be formatted. '''''Upload your slide to the wiki.'''''
** Note that since the wild type data is being analyzed by one of the groups in the class, it will be sufficient for this week to supply just the data for your strain. We will do the comparison with wild type at a later date.
* Comparing results with known data: the expression of the gene ''NSR1'' (ID: YGR159C)is known to be induced by cold shock. '''''Find ''NSR1'' in your dataset. What is its unadjusted, Bonferroni-corrected, and B-H-corrected p values? What is its average Log fold change at each of the timepoints in the experiment?''''' Note that the average Log fold change is what we called "STRAIN)_AvgLogFC_(TIME)" in step 3 of the ANOVA analysis. Does ''NSR1'' change expression due to cold shock in this experiment?
* For fun, find "your favorite gene" (from your [[Week 3]] assignment) in the dataset. '''''What is its unadjusted, Bonferroni-corrected, and B-H-corrected p values? What is its average Log fold change at each of the timepoints in the experiment?''''' Does your favorite gene change expression due to cold shock in this experiment?

==== Clustering and GO Term Enrichment with stem (part 2)====

# '''Prepare your microarray data file for loading into STEM.'''
#* Insert a new worksheet into your Excel workbook, and name it "(STRAIN)_stem".
#* Select all of the data from your "(STRAIN)_ANOVA" worksheet and Paste special > paste values into your "(STRAIN)_stem" worksheet.
#** Your leftmost column should have the column header "Master_Index". Rename this column to "SPOT". Column B should be named "ID". Rename this column to "Gene Symbol". Delete the column named "Standard_Name".
#** Filter the data on the B-H corrected p value to be > 0.05 (that's '''greater than''' in this case).
#*** Once the data has been filtered, select all of the rows (except for your header row) and delete the rows by right-clicking and choosing "Delete Row" from the context menu. Undo the filter. This ensures that we will cluster only the genes with a "significant" change in expression and not the noise.
#** Delete all of the data columns '''''EXCEPT''''' for the Average Log Fold change columns for each timepoint (for example, wt_AvgLogFC_t15, etc.).
#** Rename the data columns with just the time and units (for example, 15m, 30m, etc.).
#** Save your work. Then use ''Save As'' to save this spreadsheet as Text (Tab-delimited) (*.txt). Click OK to the warnings and close your file.
#*** Note that you should turn on the file extensions if you have not already done so.
# '''Now download and extract the STEM software.''' [http://www.cs.cmu.edu/~jernst/stem/ Click here to go to the STEM web site].
#* Click on the [http://www.sb.cs.cmu.edu/stem/stem.zip download link] and download the <code>stem.zip</code> file to your Desktop.
#* Unzip the file. In Seaver 120, you can right click on the file icon and select the menu item ''7-zip > Extract Here''.
#* This will create a folder called <code>stem</code>.
#** You now need to download the Gene Ontology and yeast GO annotations and place them in this folder.
#** Click here to download the file [https://lmu.box.com/s/t8i5s1z1munrcfxzzs7nv7q2edsktxgl "gene_ontology.obo"].
#** Click here to download the file [https://lmu.box.com/s/zlr1s8fjogfssa1wl59d5shyybtm1d49 "gene_association.sgd.gz"].
#*Inside the folder, double-click on the <code>stem.jar</code> to launch the STEM program.
# '''Running STEM'''
## In section 1 (Expression Data Info) of the the main STEM interface window, click on the ''Browse...'' button to navigate to and select your file.
##* Click on the radio button ''No normalization/add 0''.
##* Check the box next to ''Spot IDs included in the data file''.
## In section 2 (Gene Info) of the main STEM interface window, leave the default selection for the three drop-down menu selections for Gene Annotation Source, Cross Reference Source, and Gene Location Source as "User provided".
## Click the "Browse..." button to the right of the "Gene Annotation File" item. Browse to your "stem" folder and select the file "gene_association.sgd.gz" and click Open.
## In section 3 (Options) of the main STEM interface window, make sure that the Clustering Method says "STEM Clustering Method" and do not change the defaults for Maximum Number of Model Profiles or Maximum Unit Change in Model Profiles between Time Points.
## In section 4 (Execute) click on the yellow Execute button to run STEM.
##* If you get an error, there are some known reasons why stem might not work. If you had #DIV/0! errors in your input file, it will cause problems. Re-open your file and open the Find/Replace dialog. Search for #DIV/0!, but don't put anything in the replace field. Click "Replace all" to remove the #DIV/0! errors. Then save your file and try again with stem.
##* '''''This is the stopping point for the Week 8 assignment.'''''
# '''Viewing and Saving STEM Results'''
## A new window will open called "All STEM Profiles (1)". Each box corresponds to a model expression profile. Colored profiles have a statistically significant number of genes assigned; they are arranged in order from most to least significant p value. Profiles with the same color belong to the same cluster of profiles. The number in each box is simply an ID number for the profile.
##* Click on the button that says "Interface Options...". At the bottom of the Interface Options window that appears below where it says "X-axis scale should be:", click on the radio button that says "Based on real time". Then close the Interface Options window.
##*Take a screenshot of this window (on a PC, simultaneously press the <code>Alt</code> and <code>PrintScreen</code> buttons to save the view in the active window to the clipboard) and paste it into a PowerPoint presentation to save your figures.
## Click on each of the SIGNIFICANT profiles (the colored ones) to open a window showing a more detailed plot containing all of the genes in that profile.
##* Take a screenshot of each of the individual profile windows and save the images in your PowerPoint presentation.
##* At the bottom of each profile window, there are two yellow buttons "Profile Gene Table" and "Profile GO Table". For each of the profiles, click on the "Profile Gene Table" button to see the list of genes belonging to the profile. In the window that appears, click on the "Save Table" button and save the file to your desktop. Make your filename descriptive of the contents, e.g. "wt_profile#_genelist.txt", where you replace the number symbol with the actual profile number.
##** Upload these files to the wiki and link to them on your individual journal page. (Note that it will be easier to [[Week_4#Compressing_and_Decompressing_Files_with_7-Zip | zip all the files together]] and upload them as one file).
##* For each of the significant profiles, click on the "Profile GO Table" to see the list of Gene Ontology terms belonging to the profile. In the window that appears, click on the "Save Table" button and save the file to your desktop. Make your filename descriptive of the contents, e.g. "wt_profile#_GOlist.txt", where you use "wt", "dGLN3", etc. to indicate the dataset and where you replace the number symbol with the actual profile number. At this point you have saved all of the primary data from the STEM software and it's time to interpret the results!
##** Upload these files to the wiki and link to them on your individual journal page. (Note that it will be easier to [[Week_4#Compressing_and_Decompressing_Files_with_7-Zip | zip all the files together]] and upload them as one file).
# '''Analyzing and Interpreting STEM Results'''
## Select '''''one''''' of the profiles you saved in the previous step for further intepretation of the data. I suggest that you choose one that has a pattern of up- or down-regulated genes at the cold shock timepoints. '''''Each member of your group should choose a different profile.''''' Answer the following:
##* '''''Why did you select this profile? In other words, why was it interesting to you?'''''
##* '''''How many genes belong to this profile?'''''
##* '''''How many genes were expected to belong to this profile?'''''
##* '''''What is the p value for the enrichment of genes in this profile?''''' Bear in mind that we just finished computing p values to determine whether each individual gene had a significant change in gene expression at each time point. This p value determines whether the number of genes that show this particular expression profile across the time points is significantly more than expected.
##* Open the GO list file you saved for this profile in Excel. This list shows all of the Gene Ontology terms that are associated with genes that fit this profile. Select the third row and then choose from the menu Data > Filter > Autofilter. Filter on the "p-value" column to show only GO terms that have a p value of < 0.05. '''''How many GO terms are associated with this profile at p < 0.05?''''' The GO list also has a column called "Corrected p-value". This correction is needed because the software has performed thousands of significance tests. Filter on the "Corrected p-value" column to show only GO terms that have a corrected p value of < 0.05. '''''How many GO terms are associated with this profile with a corrected p value < 0.05?'''''
##* Select 6 Gene Ontology terms from your filtered list (either p < 0.05 or corrected p < 0.05).
##** Each member of the group will be reporting on his or her own cluster in your research presentation. You should take care to choose terms that are the most significant, but that are also not too redundant. For example, "RNA metabolism" and "RNA biosynthesis" are redundant with each other because they mean almost the same thing.
##*** Note whether the same GO terms are showing up in multiple clusters.
##**'''''Look up the definitions for each of the terms at [http://geneontology.org http://geneontology.org]. In your research presentation, you will discuss the biological interpretation of these GO terms. In other words, why does the cell react to cold shock by changing the expression of genes associated with these GO terms? Also, what does this have to do with the transcription factor being deleted (for the groups working with deletion strain data)?'''''
##** To easily look up the definitions, go to [http://geneontology.org http://geneontology.org].
##** Copy and paste the GO ID (e.g. GO:0044848) into the search field on the left of the page.
##** In the [http://amigo.geneontology.org/amigo/medial_search?q=GO%3A0044848 results] page, click on the button that says "Link to detailed information about <term>, in this case "biological phase"".
##** The definition will be on the next results page, e.g. [http://amigo.geneontology.org/amigo/term/GO:0044848 here].

==== Data and Files ====

Your data and files section should include:
* Your Excel workbook with all of your calculations.
** Note that you will be working with this workbook for the next week or two, adding computations to it. Save the new versions to the wiki with the '''same filename'''. The wiki will store each version of the file so you can always go back to a previous version, if need be.
* Your PowerPoint slide with a summary table of p values.
** You will also be adding to the PowerPoint presentation during subsequent steps in the analysis.
* The input .txt file that you used to run stem.


==== Conclusion (Summary Paragraph) ====

* Write a summary paragraph that gives the conclusions from this week's analysis.

== Shared Journal Assignment ==

* Store your journal entry in the shared [[Class Journal Week 8]] page. If this page does not exist yet, go ahead and create it (congratulations on getting in first ''':)''' )
* Link to your journal entry from your user page.
* Link back from the journal entry to your user page.
**'''''NOTE: you can easily fulfill the links part of these instructions by adding them to your template and using the template on your user page.'''''
* Sign your portion of the journal with the standard wiki signature shortcut (<code><nowiki>~~~~</nowiki></code>).
* Add the "Journal Entry" and "Shared" categories to the end of the wiki page (if someone has not already done so).

=== Reflect ===

Answer the following questions after you have completed the individual assignment.

# What aspect of this assignment came most easily to you?
# What aspect of this assignment was the most challenging for you?
# What (yet) do you not understand?

[[Category:Assignment]]

Skinny Genes

2019-12-08T20:16:12Z

Kdahlquist: /* Files */ link to fresh output file

{{template:Skinny Genes}}
==Positions==

'''Project Manager:''' Aby

'''Quality Assurance:''' Jonar & Christina

'''Data Analysis:''' Aby & David

'''Coder:''' Mihir
==Files==
[[Media: Ymesfin_Sample_to_Data_Relationship_Table.xlsx | Sample to Data Relationship Table]]

[[Media: Ymesfin_Yeast_Potassium_Starvation_Journal_Club_2.pdf | Journal Club Presentation]]

[[Media: Ymesfin_Barreto_ANOVA_no_duplicates_-_Copy.xlsx | ANOVA Data (MOST UPDATED)]]

[[Media: Copy_ymesfin_Baretto_yeastract_regulation_matrix.xlsx | Network Data]]

[[Media: Ymesfin_Result.pdf ‎| Stem and GRNsight Network]]

[[Media: Wt_profile39_gene_table.txt | Profile 39 Gene Table]]

[[Media: Wt_profile39_GO_table.txt | Profile 39 GO list]]

[[Media: SkinnyGenesGeneGOLists.zip | Profile 39 Gene Table & Lists (.zip)]]

[[Media: Ymesfin_profile39_gene_list.xlsx | Rank by TF list]]

[[Media: Skinny Genes Database.zip|Zip file containing the Skinny Genes group database]]

[[Media: Ymesfin_Barreto_GRNmap_Output.zip | GRNmap Output]]

[[Media: Ymesfin_Barreto_GRNmap_input.xlsx | GRNmap Input]]

[[Image: Barreto_ymesfin_colored_network.PNG]]

[[Media:Skinny_Genes_fresh_GRNmap_run.zip | fresh GRNmap output from @kdahlquist]]
* I redid the input file by copying and Paste special > Paste values into a new .xlsx file
* I then re-ran GRNmap
* Both the input and output files are visualized correctly in GRNsight. I think some formatting of the original input file that got carried through was messing up GRNsight, either that or the "threshold_b" sheet was out of order.

==Annotated Bibliography==
#Aby: Hess, D. C., Lu, W., Rabinowitz, J. D., & Botstein, D. (2006). Ammonium toxicity and potassium limitation in yeast. ''PLoS biology'', ''4''(11), e351. DOI: 10.1371/journal.pbio.0040351
#Aby: Navarrete, C., Petrezsélyová, S., Barreto, L., Martínez, J. L., Zahrádka, J., Ariño, J., ... & Ramos, J. (2010). Lack of main K+ uptake systems in Saccharomyces cerevisiae cells affects yeast performance in both potassium-sufficient and potassium-limiting conditions. ''FEMS yeast research'', 10(5), 508-517. DOI: DOI:10.1111/j.1567-1364.2010.00630.x
#Christina: Anemaet, I. G., & van Heusden, G. P. H. (2014). Transcriptional response of Saccharomyces cerevisiae to potassium starvation. ''BMC genomics'', ''15''(1), 1040. doi:10.1186/1471-2164-15-1040
#Christina: Martínez, J. L., Luna, C., & Ramos, J. (2012). Proteomic changes in response to potassium starvation in the extremophilic yeast Debaryomyces hansenii. ''FEMS yeast research'', ''12''(6), 651-661. doi: 10.1111/j.1567-1364.2012.00815.x
#Mihir: Canadell, D., González, A., Casado, C., & Ariño, J. (2015). Functional interactions between potassium and phosphate homeostasis in Saccharomyces cerevisiae. ''Molecular microbiology'', ''95''(3), 555-572. DOI: 10.1111/mmi.12886
#Mihir: Kahm, M., Navarrete, C., Llopis-Torregrosa, V., Herrera, R., Barreto, L., Yenush, L., … Kschischo, M. (2012). Potassium Starvation in Yeast: Mechanisms of Homeostasis Revealed by Mathematical Modeling. ''PLoS Computational Biology'', ''8''(6). doi: 10.1371/journal.pcbi.1002548
#David: Gladfelter, A.S., Kozubowski, L., Zyla, T.R., and Lew, D.J. (2005) Interplay between septin organization, cell cycle and cell shape in yeast. J Cell Sci 118: 1617–1628. DOI: 10.1242/jcs.02286
#David: Howell, A.S., and Lew, D.J. (2012) Morphogenesis and the cell cycle. Genetics 190: 51–77. DOI: 10.1534/genetics.111.128314
#Jonar: Hess, D. C., Lu, W., Rabinowitz, J. D., & Botstein, D. (2006). Ammonium toxicity and potassium limitation in yeast. PLoS biology, 4(11), e351.
#Jonar: Udensi, U. K., & Tchounwou, P. B. (2017). Potassium Homeostasis, Oxidative Stress, and Human Disease. International journal of clinical and experimental physiology, 4(3), 111–122. doi:10.4103/ijcep.ijcep_43_17

==Schedule==
{| class="wikitable"
|-
! Tasks
! Date
|-
| Assigment for the Week:
*Project Manager: Create Sample/Data Relationship Table
*Data Analysists: download raw data, run ANOVA
*QAs and Coder: standardize the ID and Standard Names of the genes
| 11/19/19
|-
| Assignment for the Week:
*Data Analysists: Run STEM and prepare GRNsight network
*QAs: Discuss standard column headers for database with other groups
*Coder: Design and Create Database
| 11/24/19
|-
| Assignment for the Week:
*Data Analysists: Create GRNmap Input Worksheet and run GRNmap
*QAs and Coder: Finalize the Database
| 12/03/19
|-
|Assignment for the Week:
*Data Analysists, QAs, Coder: Begin working on Research Paper and Presentation
| 12/05/19
|-
| Final Presentation
| 12/10/19
|-
| Report submitted
| 12/13/19
|-
|}

==Executive Summaries==
===Week 11===
====Aby's Reflection====
*Progress: This week we created an outline for our presentation and formatted the presentation for the journal club.
*#What worked? Responsibilities for the presentation was delegated well so that everyone contributed an equal share.
*#What didn't work? It was difficult to work around everyone's schedules so that we could meet up and work together.
*#What will I do next to fix what didn't work? We will need to communicate with one another better and anticipate the amount of time necessary to complete the assignment as a group so that our schedules don't interfere with the groupwork.
====Jonar's Reflection====
*Progress:
*#What worked? Everybody in the group was responsive and we were able to agree on and start our presentation early. The delegation of work and support worked well.
*#What didn't work? Due to time constraints with multiple people, finding time for all five members was a little difficult
*#What will I do next to fix what didn't work? We can plan an early meeting time or we can delegate tasks and plan a little meeting just to go over the work.

[[User:Jcowan4|Jcowan4]] ([[User talk:Jcowan4|talk]]) 23:59, 13 November 2019 (PST)
====Christina's Reflection====
*Progress:
*#What worked? We started early which allowed us to have the time to work through our presentation in a timely manner.
*#What didn't work? It is difficult to find a meeting time for 5 people in order to work on the presentation.
*#What will I do next to fix what didn't work? Try to find a time on the weekend to meet instead of on school days.

[[User:Cdomin12|Cdomin12]] ([[User talk:Cdomin12|talk]]) 00:51, 26 November 2019 (PST)

====Mihir's Reflection====
*Progress:
*#What worked?
*#* We were able to communicate well, and we all worked together to help each other understand and excel in our tasks. Furthermore, we figured out methods of communication and how to best work with each other remotely.
*#What didn't work?
*#* Scheduling times to work on this assignment was pretty hectic this week, given that each member of our team had an unusual number of projects, midterms, homework, and extra-curricular activities this week. We could not all meet together outside of class.
*#What will I do next to fix what didn't work?
*#* Hopefully, this was a one-time occurrence and none of us will be as busy as we were this week. However, other than that, we could also improve our responsiveness in our team's group message, as that is our sole method of communication outside of class.
[[User:Msamdars|Msamdars]] ([[User talk:Msamdars|talk]]) 20:16, 13 November 2019 (PST)
====David's Reflection====
*Progress:
*#'''What worked?''' We were able to communicate and agree to meet twice outside of class, which was the most we could do during our busy schedules. We were also able to create a GroupMe group chat so that everyone has a chance to communicate with each other and ask questions when needed.
*#'''What didn't work?''' Creating times where we could all meet up in the computer lab was very difficult because all of us had different schedules, so when we actually did make a time to meet, we made sure to use the time together as effective as possible. I wish I didn't have so much work from other classes that coincidentally built up especially in this week.
*#'''What will I do next to fix what didn't work?''' I will try to better manage my time and spending time on assignments from other classes evenly so that when it comes to working on this project with the group, I can fully focus on what I have to do instead of stressing about another project from another class.
[[User:Dramir36|Dramir36]] ([[User talk:Dramir36|talk]]) 00:01, 26 November 2019 (PST)

===Week 12/13===
====Aby's Reflection====
*Progress: This week David and I statistically analyzed the data using an ANOVA.
*#What worked? The ANOVA was relatively easy to code using Excel.
*#What didn't work? We were hoping to run STEM this week as well but had difficulty creating the ID's and Standard Names for the data.
*#What will I do next to fix what didn't work? We reached out to Dr. Dahlquist in regards to changing the ID's and Standard Names and thus, we should be able to run STEM after a few more edits.
[[User:Ymesfin|Ymesfin]] ([[User talk:Ymesfin|talk]]) 11:10, 25 November 2019 (PST)

====Jonar's Reflection====
*Progress:
*#What worked? What worked was how we divided what needed to be done and how we were in constant communication with each other.
*#What didn't work? Early on we had problems with the standard name and gene ID, which caused us to get a little anxious about what were supposed to do. In essence, we needed to ask for help. (Which was done later on but not early enough)
*#What will I do next to fix what didn't work? Overall, I think we are handling the project well but if we had to work on something it would probably better timing for project management.

[[User:Jcowan4|Jcowan4]] ([[User talk:Jcowan4|talk]]) 19:25, 25 November 2019 (PST)

====Christina's Reflection====
*Progress:
*#What worked? We were able to communicate in class to effectively figure out what we needs to be done on a timeline.
*#What didn't work? It took a long to get past the step of finalizing the data into columns that had both the IDs and standard names. However, this was due to the fact that the data sheet had many repeated genes and a variety of different names for genes.
*#What will I do next to fix what didn't work? Communicate better on how we can solve this problem as a group.

====Mihir's Reflection====
*Progress:
** This week I worked on creating the format for encoding the entries into the database.

#What worked?
#*This weekend we split up into our different guilds. I think that we worked well in those guilds, and it seemed like the pairs of Jonar and Christina and David and Aby worked well.
#What didn't work?
#*I think that this week, we were a little too fractured because of how immediately we were required to split up following the official beginning of the project. There was a lack of communication when we were discussing how to fix the gene names of the spreadsheet. Even though our communication was good last week, because of a variety of reasons I think that we were not able to effectively relate information regarding our data and formatting to one another.
#What will I do next to fix what didn't work?
#*I think this next week, we should take the time talk to each other regarding what he had accomplished between work sessions and what we planned on working on during the upcoming work session.

====David's Reflection====
*Progress:
*#'''What worked?''' We were able to communicate and agree to meet twice outside of class, which was the most we could do during our busy schedules. We were also able to create a GroupMe group chat so that everyone has a chance to communicate with each other and ask questions when needed.
*#'''What didn't work?''' Creating times where we could all meet up in the computer lab was very difficult because all of us had different schedules, so when we actually did make a time to meet, we made sure to use the time together as effective as possible. I wish I didn't have so much work from other classes that coincidentally built up especially in this week.
*#'''What will I do next to fix what didn't work?''' I will try to better manage my time and spending time on assignments from other classes evenly so that when it comes to working on this project with the group, I can fully focus on what I have to do instead of stressing about another project from another class.
[[User:Dramir36|Dramir36]] ([[User talk:Dramir36|talk]]) 00:02, 26 November 2019 (PST)

==Milestones==

====Milestones 1====
====Milestones 2====
====Milestones 3====
====Milestones 4====
====Milestones 5====
====Milestones 6====
====Milestones 7====

File:Skinny Genes fresh GRNmap run.zip

2019-12-08T20:13:59Z

Kdahlquist: fresh GRNmap run with input file that had all formatting removed by Paste values > paste special into new .xlsx file. Both the input and output now work in GRNsight

== Summary ==
fresh GRNmap run with input file that had all formatting removed by Paste values > paste special into new .xlsx file. Both the input and output now work in GRNsight

Final Project Deliverables

2019-12-05T23:25:04Z

Kdahlquist: /* PowerPoint Presentation */ change time for powerpoint uplod

{{Final Project Links}}

== Deliverables Checklist ==

# Organized Team deliverables wiki page (or other media (CD or flash drive) with table of contents)
# Group Report (''.doc'', ''.docx'' or ''.pdf'' file)
# Individual statements of work, assessments, reflections (wiki page, ''.doc'', ''.docx'', ''.pdf'', or e-mailed to Dr. Dahlquist)
# Group PowerPoint presentation (given on Tuesday, December 10, ''.ppt'', ''.pptx'' or ''.pdf'' file)
# Sample-data relationship table in Excel (''.xlsx'')
# Excel spreadsheet with ANOVA results/stem formatting (''.xlsx'')
# PowerPoint of ANOVA table, screenshots of stem results (''.pptx''), screenshot of black and white GRNsight input network and colored GRNsight output networks
# Gene List and GO List files from each significant profile (''.txt'' compressed together in a ''.zip'' file)
# YEASTRACT "rank by TF" results (''.xlsx'')
# GRNmap input workbook (with network adjacency matrix, ''.xlsx'')
# GRNmap output workbook (''.xlsx'') and output plots (''.jpg'') zipped together
# MS Access database, unified by the three teams with expression tables and metadata table(s) created (''.accdb'')
# ReadMe for the database that describes the design of the database, references the sources of the data, and has a [https://www.quackit.com/microsoft_access/microsoft_access_2016/howto/how_to_create_a_database_diagram_in_access_2016.cfm database schema diagram] (''.doc'', ''.docx'', ''.pdf'')
# Query design for populating a GRNmap input workbook from the database (screenshot of MS Access, or SQL code, ''.txt'')
# Electronic notebook corresponding to these the microarray results files ([[Week 12/13]] and [[Week 15]]) to support ''reproducible research'' so that all manipulations of the data and files are documented so that someone else could begin with your starting file, follow the protocol, and obtain your results.

=== Grading ===

* The Group Report is worth 50 points.
* The Group PowerPoint presentation is worth 50 points.
* All other deliverables listed above, together, are worth 50 points.

== Group Report ==

* The report should be written with contributions from all group members.
* Submit as ''.doc'', ''.docx'' or ''.pdf'' file.

=== Style Sheet ===

Use the following guidelines when formatting your report:
* 2.54 cm (1 in) margins on all sides
* Double-spaced
* 12 point Times/Times New Roman font
* Number the pages on the lower-right corner
* Use left justification (“jagged” on the right side)

=== Title Page ===

Include the following information in a standalone title page:
* A descriptive title for your project
** The function of the title is to identify the main result or take-home message of the paper. It should be as specific as possible. It can be a phrase or a sentence. What is the main result of your paper that you want to convey with the title?
* The names of the team members (with middle initials)
* The course number and title of the class
* The date of submission

=== Introduction ===

The introduction gives the background information necessary to understand your report and should be '''2-3''' pages long (double-spaced). The introduction should be in the form of a logical argument that “funnels” from broad to narrow:

<gallery mode="nolines" widths=322px heights=256px>
Funnel.jpg
</gallery>

* States importance of the problem
* States what is known about the problem
** Give an overview of what is known about the regulation of the transcriptional response in yeast from your team's journal article and the annotated bibliography your team developed.
** Discuss how you will approach this by re-analyzing the yeast microarray data from your article.
* States what is unknown about the problem
** Which transcription factors belong in the gene regulatory network that controls the response in yeast is still unknown.
* States clues that suggest how to approach the unknown
** Introduce GRNmap and GRNsight as the answer to this problem.
* States the question the paper is trying to address
** In this case you want to discover new information about the microarray data using GRNmap and GRNsight.
* State the approach
** Creating an MS Access database to facilitate making a GRNmap input workbook that can be used by your group and other scientists in the future.

=== Combined Methods/Results/Discussion section ===

This section will summarize the entire workflow and findings for the project with contributions from all team members (Data Analyst, QA, and Coder/Designers). Create a combined flow chart of the tasks/milestones of each of the team members and then briefly describe the flow chart in the text.
* Number each of the figures sequentially and number each of the tables sequentially in order from first mention in the text. You can either embed your figures and tables in the appropriate place in the text or put them all at the end. Do not mix both styles, however.
* Write a descriptive legend for each figure and table that briefly states what the figure/table is and gives a brief key to any labels and abbreviations.

==== Data Analyst ====

* Table of ANOVA results from the [[Week 8]], discussing the interpretation of the p values.
** With an ANOVA p value < 0.05, are there more than 5% of the genes with a significant change in gene expression at any timepoint?
** Compare with what the authors of the paper considered a meaningful gene expression change.
* From the STEM analyis, include as figures the overall results (the screenshot showing all of the clusters) and then focus on the ones you interpreted for the [[Week 10]] journal assignment.
** Were there clusters shown in your journal article? How do they compare with the ones you got?
** Include a table showing the GO results for that cluster (just the narrowed down list of terms that you have interpreted).
** Discuss what the p values for the cluster and for the GO term list mean.
** Discuss the biological interpretation of your GO terms.
** Were there GO/MIPS terms in your article? Compare your terms with the article.
* Include a table that lists the transcription factors in your final network and their enrichment p value from YEASTRACT.
** Describe how and why you and your partner chose these transcription factors for your network.
** Include a figure (screenshot) of the unweighted networks visualized with GRNsight.
** What transcription factors were mentioned in your article? Are yours the same or different?
* GRNmap results
** Show the GRNsight visualization (screenshot) of the weighted networks, making sure that the genes are placed in the same relative location as each other an as the unweighted network figure.
** Provide the LSE:minLSE ratio
** Provide a table of the weights, P's, and b's
** Organize and show the individual expression plots
** Interpret the results of the model
*** What seem to be the most important transcription factors in the network? How does that compare with the journal article?

==== Coder/Designers ====

* Give a narrative description of the database, including the design of the expression tables and metadata table(s).
* Provide a database schema.

==== QA ====

* Describe the QA process for the database, noting any issues that were encountered, and stating whether data are complete or not.
** Refer to the sample-data relationship table to discuss how microarray data format was regularized
** Discuss whether all yeast gene IDs were imported into the database and any formatting issues

=== Conclusions ===

* Write a '''1-2''' page conclusion that summarizes the overall project and your findings.
** Overall, what have you learned about the biological phenomenon described in your journal club article?
** How does the new database facilitate the data analysis?
** What future directions would you take if you were to continue this project?
** Relate the results of your project to the papers you presented for journal club and included in your bibliography in [[Week 11]].
*** Did you discover anything new that wasn't reported in the journal club paper (Data Analyst/QA)?

=== Acknowledgments ===

Write a short paragraph acknowledging the assistance of anyone who is not a member of your team.

=== References ===

* This section lists all of the references cited in the text of the report (and only those references cited in the paper). Follow the [[Media: BIOL367_Fall2019_GuidelinesforLiteratureCitations.pdf | Guidelines for Literature Citations in a Scientific Paper]] handout for general principles.
* Remember that you need to cite anything for which you are not the original source. Generally, in the introduction, you should aim for a minimum of two in-text citations per paragraph. You may reference the course web site using the appropriate format for a web reference.
* List your references in alphabetical order by first author using the [https://owl.purdue.edu/owl/research_and_citation/apa_style/apa_formatting_and_style_guide/reference_list_basic_rules.html APA style] that you have been using throughout the semester. Note that the proper format must be followed for both articles and websites, just URLs are not sufficient.

== PowerPoint Presentation ==

Each team of students will prepare and give a 20 minute PowerPoint presentation to report the results of their project on Tuesday, December 10 at 2:00-4:00 PM.
* Please follow the [http://brightspace.lmu.edu Presentation Guidelines on Brightspace] for how to format your slides.
* You will need to prepare ~20 slides (assume 1 slide per minute of presentation) and include the following content:
** Title slide that gives the main take-home message as the title of your presentation, the authors, date, and venue (course number and title).
** Outline slide that is a summary of take-home messages of your talk (should mirror your conclusion slide)
** The body of your talk (organized in a logical flow, not necessarily in the order given below).
*** Introduce the importance/significance of the problem and give background on the experiment in your journal article. You can draw from the your journal club presentation. This should follow the logic of the Introduction section of your group paper.
*** Show a combined flow chart of the tasks/milestones completed by your group (see Materials and Methods of group paper).
*** Show the table of ANOVA results.
*** Show the screenshot of the overall clustering results and the cluster you focused on.
*** Show a table of the GO results from that cluster, giving an interpretation.
*** Show the table of regulatory transcription factors in your network and their p values for enrichment.
*** Show the unweighted and weighted networks in GRNsight (with the genes arranged in the same way in each figure)
*** Database schema diagram for the MS Access database
** Conclusion slide that mirrors your outline
** Future directions
** Acknowledgments
** References
* '''''Your PowerPoint slides must be uploaded to the wiki and linked to from your individual journal page and your team page by 12:00pm (noon), Tuesday, December 10.'''''
** You can update your slides before your presentation, but we will be grading the ones you upload by the deadline.
* Your presentation (both the slides and the oral presentation) will be evaluated by the instructors using the [[Presentation Rubric]].
* Your presentation will also be evaluated by your fellow classmates (anonymously) who will answer the following questions:
*# What is the speaker's take-home message (one short sentence)?
*# What are the best points about the presentation's organization, visuals, and delivery? Please give at least 2 specific examples.
*# What points need improvement? Please give at least 2 specific examples.
* We expect that you will take the feedback from your previous presentation into account when doing this presentation.

== Individual Assessment and Reflection ==

Each person on the team will complete an assessment and reflection ''individually''. If you are comfortable with making this assessment publicly available, you may write it up as a wiki page or as a Word document uploaded to your group deliveables page. If you prefer to communicate your assessment privately, then email this to both Drs. Dahlquist and Dionisio.

=== Statement of Work ===

* Describe exactly what you did on the project.
* Provide references or links to artifacts of your work, such as:
** Wiki pages
** Other files or documents
** Code or scripts

=== Assessment of Project ===

* Give an objective assessment of the success of your project workflow and teamwork.
* What worked and what didn't work?
* What would you do differently if you could do it all over again?
* Evaluate your team’s portion of the Final Project and Group Report in the following areas:
*# Content: What is the quality of the work?
*# Organization: Comment on the organization of the project and of your group's wiki pages.
*# Completeness: Did your team achieve all of the project objectives? Why or why not?

=== Reflection on the Process ===

* What did you learn?
** With your head (biological or computer science principles)
** With your heart (personal qualities and teamwork qualities that make things work or not work)?
** With your hands (technical skills)?
* What lesson will you take away from this project that you will still use a year from now?

{{Final Project Links}}

[[Category:Group Projects]]

Main Page

2019-12-05T22:34:28Z

Kdahlquist: /* Upcoming Seminars */ no more seminars

__NOTOC__
<div style="text-align: center; font-size: 175%">Loyola Marymount University</div>
<div style="text-align: center; font-size: 250%; line-height: 1.25em">'''BIOL 367-01: Biological Databases'''</div>
<div style="text-align: center; font-size: 175%; margin-bottom: 0.85em">Fall 2019</div>

<div class="toc-container">__TOC__</div>

== Announcements ==

* Please complete the course evaluation on [http://brightspace.lmu.edu Brightspace] by class time on Thursday.
* Data Analysts and QA's who have a partner in their group can have a shared ''individual'' journal entry. Both students will be given the same grade and are expected to contribute equally to the electronic lab notebook.

* '''The combined database may be found [[Media:BIOL 478 - BioDB CombinedDatabase.zip|here]].'''

=== Upcoming Seminars ===

* There are no further seminars.

== Instructor ==
* [[User:Kdahlquist|Kam D. Dahlquist]]

== Students ==

{| style="margin-left: auto; margin-right: auto"
| style="padding-right: 1em" | [[User:Marmas|Michael Armas]]
| style="padding-right: 1em" | [[User:Cdomin12|Christina Dominguez]]
| style="padding-right: 1em" | [[User:knguye66|Kaitlyn Nguyen]]
| style="padding-right: 1em" | [[User:Ntesfaio|Naomi Tesfaiohannes]]
|-
| style="padding-right: 1em" | [[User:mavila9|Marcus Avila]]
| style="padding-right: 1em" | [[User:Imacarae|Ivy-Quynh Macaraeg]]
| style="padding-right: 1em" | [[User:Jnimmers| John Nimmers-Minor]]
| style="padding-right: 1em" | [[User:eyoung20|Emma Young]]
|-
| style="padding-right: 1em" | [[User:Jcowan4|Jonar Cowan]]
| style="padding-right: 1em" | [[User:Dmadere | DeLisa Madere]]
| style="padding-right: 1em" | [[User:dramir36| David Ramirez]]
| style="padding-right: 1em" |
|-
| style="padding-right: 1em" | [[User:Icrespin| Iliana Crespin]]
| style="padding-right: 1em" | [[User:ymesfin| Yeabsira Mesfin]]
| style="padding-right: 1em" | [[User:msamdars| Mihir Samdarshi]]
| style="padding-right: 1em" |
|}

{{Final Project Links}}

== Course Schedule ==

The current course schedule is shown below. In addition to the listed in-class agendas, the following guidelines are also notable:

* Unless otherwise stated on the schedule, your weekly journals/assignments are due on Thursdays at 12:01am. In cases where subsequent revisions or corrections will be accepted, the instructor will provide feedback and submission deadlines on a per-assignment basis.
* Reading assignments should be completed ''before'' coming to class.

=== Part 1: Building Blocks (Genetic Code and Manipulating Text) ===

{| class="schedule-table"
! #
! Date
! Reading
! Agenda
! Journal
|-
! 1
| Tuesday<br/>8/27/2019
|
|
* Syllabus walkthrough
* Pairwise interviews and introductions
* Academic Honesty case studies
* Class wiki account: Set up a username and password for this wiki site
| rowspan="2" | [[Week 1]]
(due at 12:01am 9/5)

[[Class_Journal_Week_1 | Class Journal Week 1]]
|-
!
| Thursday<br/>8/29/2019
| [http://brightspace.lmu.edu Chapter 1 of ''On Becoming a Biologist''] by John Janovy, Jr. (on Brightspace)

[http://dl.acm.org/ft_gateway.cfm?id=1378711 ''Voices of Computing''] and [http://dl.acm.org/ft_gateway.cfm?id=1272529 ''Computing is a Natural Science''] by Peter Denning (these links should be accessed from within LMU to get the full article)

|
* What is a model?
** Model of a cell/model of a computer

** [http://brightspace.lmu.edu Slides on Brightspace]

{{Course Schedule Divider}}
|-
! 2
| Tuesday<br/>9/3/2019
|
|
* Discuss the [[Week 1]] assignment
** Wiki syntax overview
** Lab session to setup User wiki page

| rowspan="2" | [[Week 2]]
(due at 12:01am 9/12)

[[Class Journal Week 2]]

|-
!
| Thursday<br/>9/5/2019
|
* [http://www.ncbi.nlm.nih.gov/books/NBK21134/#A5234 Ch. 1 ''Genomes 2'']
* [http://www.ncbi.nlm.nih.gov/books/NBK21121/ Brown, T.A. (2002) ''Genomes 2'', Ch. 3, especially section 3.3.2]
* On [http://brightspace.lmu.edu Brightspace]:
** Nirenberg (2004) ''Deciphering the Genetic Code''
** Kaji & Kaji (2004) ''Setting the Record Straight''
** Moody (2004) ''Digital Code of Life'', Ch. 1
** Hayes (2004) ''Ode to the Code''

|
* DNA structure
** [http://brightspace.lmu.edu Slides on Brightspace]

{{Course Schedule Divider}}
|-
! 3
| Tuesday<br/>9/10/2019
|
|
* Aipotu work session

| rowspan="2" | [[Week 3]]
(due at 12:01am 9/19)

[[Class Journal Week 3]]

|-
!
| Thursday<br/>9/12/2019
|
|
* Genes as models
* Central model of molecular biology
** [http://www.youtube.com/watch?v=u9dhO0iCLww Berkeley translation video]
** This [http://www.dnai.org/a/index.html web site] has links to animations of the replication, transcription, and translation processes.
** [http://brightspace.lmu.edu Slides on Brightspace]
* [[Media:BIOL367_Fall2019_GeneticCode-CentralModel-2.pdf | Paper and pencil exercise]]

{{Course Schedule Divider}}
|-
! 4
| Tuesday<br/>9/17/2019
|
* [http://www.bloomberg.com/graphics/2015-paul-ford-what-is-code/ Ford, Paul. “What is Code?” ''Business Week'', June 11, 2015.]
* [https://www.dataone.org/sites/all/documents/education-modules/pptx/L01_DataManagement.pptx DataONE: Data Management Slides]
|
Introduction to biological databases (part 1)
* What is bioinformatics?
* Open Science Ecosystem
* DataONE: Data Management and the life cycle of data
* Slides are on [http://brightspace.lmu.edu Brightspace]

| rowspan="2" | [[Week 4]]
(due at 12:01am 9/26)

[[Class Journal Week 4]]

|-
!
| Thursday<br/>9/19/2019
|
| Introduction to biological databases (part 2)
* [http://dondi.lmu.build/share/intro/wheres-my-stuff.pdf Where's my Stuff?]
* Slides are on [http://brightspace.lmu.edu Brightspace]
* An overview of computers, networks, files, web applications, web services, and databases
* Names, URLs, identifiers
** As applied to biology: genes, species
* “Stuff” on the worldwide web
** Accessing the web on a browser

{{Course Schedule Divider}}
|-
! 5
| Tuesday<br/>9/24/2019
|
|
[[ Week 4]] work session
| rowspan="2" | [[Week 5]]
(individual due at 12:01am 10/1)

'''''Note Tuesday Due Date'''''

[[Class Journal Week 5]]

(shared due at 12:01am 10/3)

|-
!
| Thursday<br/>9/26/2019
| [https://academic.oup.com/nar/article/47/D1/D1/5280358 The 26th annual Nucleic Acids Research database issue]

[http://www.oxfordjournals.org/nar/database/a/ The NAR Molecular Biology Database Collection]

|
Introduction to biological databases (part 2)
* Terminology, types, evaluation, four "Gold Standard" databases
* A closer look at evaluating biological databases (slides on [http://brightspace.lmu.edu Brightspace])
* [http://www.dataone.org/sites/all/documents/L02_DataSharing.pptx DataONE: Data Sharing]
* [http://brightspace.lmu.edu Presentation Guidelines PowerPoint on Brightspace]

{{Course Schedule Divider}}
|-
! 6
| Tuesday<br/>10/1/2019
|
| Database presentations part 1
# group 1
# group 2
# group 3
# group 4
| rowspan="2" | [[Week 6]]
(due at 12:01am 10/10)

[[Class Journal Week 6]]
|-
!
| Thursday<br/>10/3/2019
|
| Database presentations part 2
# group 1
# group 2
# group 3

[[Presentation Rubric]]

[http://brightspace.lmu.edu/ Presentation Guidelines on Brightspace]

|}

=== Part 2: Going Deeper (Gene Expression Data and Relational Databases) ===

{| class="schedule-table"
! #
! Date
! Reading
! Agenda
! Journal
|-
! 7
| Tuesday<br/>10/8/2019
| For more on the Human Genome Project, see:
* Moody (2004) Chapter 6 (on [http://brightspace.lmu.edu Brightspace])
* [http://www.nature.com/news/2010/100623/pdf/4651000a.pdf Science After the Sequence]
* [http://www.nature.com/news/2010/100623/full/4651000a.html Nature Special: Human Genome at 10]
* [http://www.sciencemag.org/content/vol291/issue5507/index.dtl Human Genome Project articles in ''Science'']
* [http://www.nature.com/nature/journal/v409/n6822/index.html Human Genome Project articles in ''Nature'']
|
* Human Genome Project to DNA microarrays (slides on [http://brightspace.lmu.edu Brightspace])
| rowspan="2" | [[Week 7]]
(due at 12:01am 10/17)

[[Class Journal Week 7]]

|-
!
| Thursday<br/>10/10/2019
| [http://www.ncbi.nlm.nih.gov/books/NBK26818/#A1633 Alberts ''et al''. (2002) ''Molecular Biology of the Cell'', Ch. 8: Microarrays]

[http://www.nature.com/ng/journal/v21/n1s/full/ng0199supp_33.html Brown & Botstein (1999) "Microarrays" ''Nature Genetics'']

Campbell & Heyer Chapter 4 (on [http://brightspace.lmu.edu on Brightspace])

DeRisi et al. (1997) [http://www.sciencemag.org/content/278/5338/680.full ''Science'' 278: 680-686.]
| Review [[Week 6]] assignment

{{Course Schedule Divider}}
|-
! 8
| Tuesday<br/>10/15/2019
| [http://www.dataone.org/sites/all/documents/L04_DataEntryManipulation.pptx DataONE: Data Entry and Manipulation]
| More about DNA microarray analysis
* Slides on [http://brightspace.lmu.edu Brightspace]
| rowspan="2" | [[Week 8]]
(due at 12:01am 10/24)

[[Class Journal Week 8]]
|-
!
| Thursday<br/>10/17/2019
|
| Begin DNA microarray analysis part 1
* Statistical analysis of Dahlquist Lab yeast cold shock dataset
* Test your understanding: [http://xkcd.com/882/ http://xkcd.com/882/]

{{Course Schedule Divider}}
|-
! 9
| Tuesday<br/>10/22/2019
| [https://academic.oup.com/bioinformatics/article/21/suppl_1/i159/203147 Ernst & Bar-Joseph (2006) STEM: a tool for the analysis of short time series gene expression data.]
|
Microarray Data Analysis part 2
* Clustering with stem software



| rowspan="2" | [[Week 9]]
(due at 12:01am 10/31)

[[Class Journal Week 9]]

|-
!
| Thursday<br/>10/24/2019
|
* [https://academic.oup.com/nar/article/46/D1/D348/4191333 Teixeira, et al. (2017). YEASTRACT: an upgraded database for the analysis of transcription regulatory networks in ''Saccharomyces cerevisiae'']
* [https://peerj.com/articles/cs-85/ Dahlquist et al. (2016) GRNsight: a web application and service for visualizing models of small- to medium-scale gene regulatory networks]
|
Microarray Data Analysis part 3
* Finding a candidate GRN ([http://www.yeastract.com/ YEASTRACT])

{{Course Schedule Divider}}
|-
! 10
| Tuesday<br/>10/29/2019
| [https://link.springer.com/article/10.1007/s11538-015-0092-6 Dahlquist et al. (2015) Parameter Estimation for Gene Regulatory Networks from Microarray Data: Cold Shock Response in ''Saccharomyces cerevisiae'']
|
Microarray Data Analysis part 4
* Visualizing the candidate GRN ([https://dondi.github.io/GRNsight/ GRNsight])
* Dynamical systems modeling of the gene regulatory network ([http://kdahlquist.github.io/GRNmap/ GRNmap])

| rowspan="2" | [[Week 10]]
(due at 12:01am 11/7)

[[Class Journal Week 10]]
|-
!
| Thursday<br/>10/31/2019
|
|
Microarray Data Analysis part 5
* Visualizing and interpreting the modeling results ([https://dondi.github.io/GRNsight/ GRNsight])
* Group requests due in class (collected on notecards)
|}

=== Part 3: Integrating for Research (GRNsight Gene Expression Database Project) ===

{| class="schedule-table"
! #
! Date
! Reading
! Agenda
! Journal
|-
! 11
| Tuesday<br/>11/5/2019
|
| Dynamical Systems Modeling with GRNmap
| rowspan="2" | [[Week 11]]

(due at 12:01am 11/14)

'''''Thursday Due Date'''''
|-
!
| Thursday<br/>11/7/2019
|
|
Begin GRNsight Expression Database Project
* Team assignments
* Overview of project
* Work session to create Team wiki pages

{{Course Schedule Divider}}
|-
! 12
| Tuesday<br/>11/12/2019
|
|
Literature searching and annotated bibliographies
* ''Course LibGuide:'' http://libguides.lmu.edu/BIOL367
* Select microarray papers for journal club

| rowspan="2" | [[Week 12/13]]

(due at 12:01am 11/26)

'''''Note Tuesday Due Date'''''

|-
!
| Thursday<br/>11/14/2019
|
| Journal Club Presentations

[[Presentation Rubric]]

[http://brightspace.lmu.edu Presentation Guidelines PowerPoint on Brightspace]

{{Course Schedule Divider}}
|-
! 13
| Tuesday<br/>11/19/2019
|
|
* Status report
* Work session

| rowspan="2" | [[Week 12/13]]

(due at 12:01am 11/26)<br/>
'''''Note Tuesday due date'''''
|-
!
| Thursday<br/>11/21/2019
|
|
* Status report
* Work session

|-
! 14
| Tuesday<br/>11/26/2017
|
|
* Status report
* Work session
| '''''No Week 14 assignment'''''
|-
! style="background: #ded" |
| style="background: #efe; border: none" | Thursday<br/>11/28/2019
| style="background: #efe; border: none" |
| style="background: #efe; border: none" | Thanksgiving—no class
| style="background: #efe; border: none" |
{{Course Schedule Divider}}
|-
! 15
| Tuesday<br/>12/3/2019
|
|
* Status report
* Work session
| rowspan="2" | [[Week 15]]

(due at 4:00pm 12/13)
|-
!
| Thursday<br/>12/5/2019
|
|
* Status report
* Work session
{{Course Schedule Divider}}
|-
! style="background: #edd" | F
| style="background: #fee; border: none" | Tuesday<br/>12/10/2019
| style="background: #fee; border: none" |
| style="background: #fee; border: none" | '''Final project presentations 2:00-4:00 PM'''
| style="background: #fee; border: none" |
|-
!
| Friday<br/>12/13/2019 4:00 PM
|
| '''Project deliverables due 4:00 PM'''
|
|}

== Course Information ==

=== Instructor ===

==== [[User:Kdahlquist | Kam D. Dahlquist, Ph.D.]] ====
: http://kdahlquist.github.io/DahlquistLab/index.htm
: '''Phone:''' (310) 338-7697
: '''E-mail:''' kdahlquist at lmu dot edu
: '''Office:''' Life Sciences Building 289
: '''Office Hours (Fall 2019):''' Mondays & Wednesdays 2:00-3:00 PM, Tuesdays & Thursdays 4:00-5:00 PM, and by appointment; I keep a sign-up sheet next to my office door to facilitate appointment-making.

=== Prerequisites/Recommended Background ===

To take this course, you must have upper division standing in the Seaver College of Science and Engineering. Otherwise, there are no strict prerequisites. Backgrounds in biology and computer science, as well as prior experience with web, database, or information management applications, may be helpful but not necessary.

=== Class Meetings and Attendance ===
TR 2:40–3:55pm, Seaver 120 

This is a hands-on, participatory course, thus attendance at all class meetings is required. Each student is allowed two “sick” days (automatically excused absences) during the semester. Further unexcused absences from class will result in a 5% deduction from the overall course grade for each absence. Every effort should be made to attend class on oral presentation days as the content of that day's class is dependent on student participation. Unexcused absences from an oral presentation will result in a grade of zero for the presentation. The instructor should be notified as soon as possible, electronically or by phone, of the reasons for all absences.

Note that the last day to add or drop a class without a grade of W is August 30. The withdrawal or credit/no-credit status deadline is November 1.

=== Mutual Responsibilities ===

This course is designed to foster your development as a biologist and computer scientist and to give you an authentic research experience. We will be engaged together in discovering, examining, and practicing the personal qualities, technical skills, and community standards of the scientific community. While you are ultimately responsible for your own learning, you are not alone. Our class constitutes a team where we will be learning from each other. The role of the instructor is to provide the expert coaching to support and assist you on your journey. All of the exercises, readings, assignments, and policies detailed below have been designed with this purpose in mind.

=== Classroom Conduct ===

We are all responsible for maintaining a classroom and laboratory environment that is safe and conducive to learning. As such, we will observe the following:

# As an LMU Lion, by the [http://studentaffairs.lmu.edu/wellness/lmucares/aboutlmucares/livethelionscode/ Lion’s code], you are pledged to join the discourse of the academy with honesty of voice and integrity of scholarship and to show respect for staff, professors, and other students.
# You are responsible for your own learning and for being a good class citizen.
# Class will start promptly on time.
# You are expected to come to class having done the assigned reading and preparatory work so that you are ready to participate in discussions and to perform the laboratory exercises.
# You are expected to bring the required materials to each class session.
# Cell phones and other ancillary devices must be turned off and put away out of sight. Your own laptop and/or tablet may be used to conduct the class exercises, '''''provided that you have confirmed with the instructor that you have the correct versions of software installed for the exercise'''''. If, however, the laptops/tablets are being used for other purposes and become distracting to you or others, you will be asked to put them away.
# All students are governed by [https://studentaffairs.lmu.edu/media/studentaffairs/osccr/documents/1920-LMU-community-standards.pdf LMU Community Standards publication]. Disruptive behavior which is persistent or significantly interferes with classroom activities may be subject to disciplinary action. A student may be referred to the Office of Student Judicial Affairs if his or her behavior constitutes a violation of the conduct code.

=== Course Website ===

This is the course web site and wiki, hosted by http://xmlpipedb.cs.lmu.edu/biodb/fall2019/. You will need to have a user account on this wiki to be able to edit the wiki and complete coursework. Updates to the course schedule and electronic copies of all handouts, assignments, and readings will be posted to this site. You will also use the site to keep an electronic lab notebook/journal for the course. In addition, students have been automatically enrolled in the course page on [http://brightspace.lmu.edu Brightspace]. The site will be used for materials that cannot be made public on this wiki, including grades.

=== Email Communication ===

At times we will communicate with the entire class using campus email systems, so it is essential that you regularly check your lion.lmu.edu email address or forward your lion account email to your preferred email address. Messages sent to the instructor at night or on the weekend will be answered the next school day.

=== Required Materials ===

==== Texts ====

There is no required text to purchase for the course; materials will be put on reserve at Hannon Library or will be available online on this wiki or [http://brightspace.lmu.edu Brightspace] site. Specific reading assignments are given on the course schedule and should be completed before coming to class.
* Assorted handouts, articles, and sample code will be distributed throughout the semester.
* Additional information is also available on the web; do not hesitate to look for further sources of information regarding the concepts, techniques, tools, and paradigms that we will discuss.

==== Materials (must be brought to each class meeting) ====

* 3-ring binder with all course handouts
* Pen, pencil, extra paper
* USB flash drive to store data
* Account for this wiki
* [http://www.github.com GitHub account]

=== Course Description ===

The disciplines of biology and computer science come together in bioinformatics, where computational tools are needed to manage and analyze the flood of data coming from new genomics technologies. Biological databases form a significant part of this young and exciting field. This course introduces students to both the biology and computer science expertise needed to understand, use, and develop biological databases. Biology topics include the fundamentals of genetics, molecular biology, and biochemistry needed to understand the data stored in biological databases, as well as the biotechnologies used to gather these data in a high-throughput manner. Computer science topics include what biological databases are, why they are important (and needed), and the challenges that arise in compiling them effectively. Biology and computer science lectures on topics that are relevant to biological databases are coupled with hands-on experience with a variety of software packages ranging from bioinformatics utilities to general-purpose database and software development tools. After learning how to use a biological database, students will be asked to build one of their own.

==== Course Objectives and Learning Outcomes ====

This course is built upon L. Dee Fink’s ''taxonomy of significant learning'', as applied to biological databases. Long after the course concludes, our hope is that:
* You understand how biological information is encoded in the genome and can apply this knowledge to a variety of biological tasks and problems
* You understand the core concepts, structure, and functions of a database, ranging from individual files to a full relational database management system, and can perform useful tasks with such data
* You show discipline and proficiency in day-to-day science and engineering best practices, such as maintaining journals and notebooks, managing your files and code, and critically evaluating scientific and technical information
* You recognize and care about how the biological and technological issues presented in this course relate to and affect society, our daily lives, and ourselves
* You have some skills and tools for “leaving your comfort zone,” flourishing outside of it, and learning more about biology and computer science on your own
* You learn how to communicate and work effectively with colleagues from different disciplines

==== University Core Curriculum ====

This course fulfills the following requirements in the University Core Curriculum:
* Integrations: Interdisciplinary Connections
* Upper Division Information Literacy Flag
* Upper Division Oral Communication Flag

=== Course Work and Grading ===

Your work in this course will be assessed in the following areas:

{| style="border: 1px dashed rgb(47, 111, 171); background: rgb(249, 249, 249); margin: 1em 0; padding: 1em" cellpadding="5ex"
| Weekly electronic lab notebook/journal assignments, individual (usually 10 points each)
| style="text-align: right" | 155 
| points
|-
| Weekly electronic lab notebook/journal assignments, shared (usually 3 points each)
| style="text-align: right" | 43 
| points
|-
| Oral presentations
| style="text-align: right" | 120
| points 
|-
| Final Project Deliverables (including written report)
| style="text-align: right" | 100
| points
|-
| '''Total'''
| style="text-align: right" | '''418''' 
| '''points'''
|}

Final course grading scale:
94.0-100.0% A
90.0- 93.9% A-
86.0- 89.9% B+
82.0- 85.9% B
78.0- 81.9% B-
74.0- 77.9% C+
70.0- 73.9% C
67.0- 69.9% C-
60.0- 66.9% D
≤ 59.9% F

Current grades will be made available at [http://brightspace.lmu.edu Brightspace].

==== Electronic Laboratory Notebook/Journal ====

One of the most important skills you can develop as a scientist is keeping an excellent laboratory notebook. For computational research, the equivalent of the biology paper-based lab notebook is documentation of your “workflow.” For this course you will practice documentation skills by keeping an electronic lab notebook or journal. The technology we will use is this wiki. We will create and edit during the semester. The wiki software is the same one that runs Wikipedia and many other wiki sites, so proficiency gained here is easily transferable to other applications.

You will create an individual user page and make weekly entries that the instructor will read and grade. You will use the wiki to complete the assignments as well. The following guidelines apply:
* Your weekly journal entry is typically due every 12:01am on Thursday Pacific time; consult the schedule for specific due dates for each assignment.
* Each weekly assignment has an individual component and a shared component. You will earn 10 points per weekly submission for the individual journal entry and 3 points per submission for the shared journal entry. '''''Late journal entries will be accepted up to one week later for up to half credit.'''''
* The instructor will read and comment on how to improve your journal entries on your user talk pages.
* Depending on the type of assignment for that week, you may be given the opportunity to make improvements to previous journal entries as the semester progresses.
* For most weeks in the semester, you will be assigned a "homework partner". You will be expected to consult with your partner in order to complete the assignment. However, unless otherwise stated, each partner must submit his or her own work as the individual journal entry (direct copies of each other's work is not allowed).
* Generally, your journal entries will consist of:
** Your electronic laboratory notebook (workflow and other documentation) for hands-on exercises and projects
** Answers to any specific questions posed in the exercise
** Acknowledgments section (see [[Week 1]] assignment for details)
** References section (see [[Week 1]] assignment for details)
** Shared reflection on your learning, assigned readings, or ethics case studies.

==== Oral Presentations ====

You will give three oral presentations in this course. The first two will be in the format of a “Journal Club” presentation where students will present and lead discussion of research articles from the primary literature. The third will be a research presentation on your final project. '''''Because that day’s class content is dependent upon each student being ready to present and lead discussion, late journal club presentations will not be accepted. An unexcused absence from a journal club presentation will result in a grade of zero for the presentation.'''''

==== Final Group Project ====



In addition to the research presentation, the culmination of your final project will be the preparation of a written laboratory report in the style of a manuscript that could be submitted to a peer-reviewed journal. Specific instructions will be posted later in the semester. '''''The Final Written Report cannot be accepted any later than Friday, December 13 at 4:00pm. The Final Written Report must be completed to receive a passing grade in the course.'''''

==== Extra Credit ====

Students may accumulate up to 12.5 points toward their final grade in extra credit by attending Biology Department seminars and completing the seminar sheets. Each seminar attended is worth 2.5 points with up to 5 seminars (12.5 points) total. You must attend the entire seminar from start to finish and personally turn in your seminar sheet to a faculty member at the end of the seminar.

Certain non-Biology Department seminars may be approved in advance for extra credit at the instructor's discretion. To receive credit for these seminars, you must turn in a one-page '''''hard copy''''' of your summary of the seminar '''''in class, within one week''''' of the date of the seminar or they will not count as extra credit.

=== Work Load Expectations ===

In line with [http://www.lmu.edu/Assets/LMU+Credit+Hour+Policy_Final.pdf LMU’s Credit Hour Policy], the work load expectation for this course is that for every one hour (50 minutes) of classroom instruction, you will complete a minimum of two hours of out-of-class student work each week. This is a 3-unit course with 3 hours (150 minutes) of instruction per week. Thus the expectation is that you will complete 6 hours of work outside of class per week.

=== University Policy on Academic Honesty ===

Loyola Marymount University is a community dedicated to academic excellence. Academic honesty in scholarship and creative work stands at the center of LMU's academic life, and is essential for true learning and creation of knowledge to take place. As a university in the Jesuit and Marymount traditions, this community expects its members to act in accordance with the highest standards of honesty and ethics at all times. Violations of academic honesty undermine the fundamental educational mission of the University and cannot be tolerated.

Academic dishonesty will be treated as an extremely serious matter with severe consequences. '''''The minimum penalty for an instance of academic dishonesty in this course, even on a 1-point assignment or extra credit assignment, is a one-letter grade penalty in the course and a zero on the assignment.''''' It is never permissible to turn in any work that has not been authored by the student, such as work that has been copied from another student or copied from a source (including Internet) without properly acknowledging the source. It is the student's responsibility to make sure that your work meets the standard set forth in the “Academic Honesty Policy” (see http://academics.lmu.edu/honesty.) You are responsible for contacting the instructor before assignments are due to proactively resolve any questions you may have.

You are required to sign the [[Media:BIOL367_Fall2019_AcademicHonestyAgreement.pdf|Academic Honesty Agreement]] for this course.

==== Academic Honesty Resources ====
* [[Media:BIOL367_Fall2019_AcademicHonestyAgreement.pdf|Academic Honesty Agreement]]
* [http://academics.lmu.edu/media/lmuacademics/academichonesty/documents/Academic%20Honesty%20Policy%20FINAL%20Appendices--051116.pdf LMU Academic Honesty Policy and Procedures]
* [http://www.bio.davidson.edu/dept/plagiarism.html The Davidson College Department of Biology Statement on Plagiarism]
* [[Media:BIOL367_Fall2019_GuidelinesforLiteratureCitations.pdf | Guidelines for Literature Citations in a Scientific Paper]]
** [https://owl.purdue.edu/owl/research_and_citation/apa_style/apa_formatting_and_style_guide/reference_list_basic_rules.html APA Style for References]
* [http://dondi.lmu.build/share/misc/plagiarism.pdf Notes on plagiarism from an electrical engineering & computer science perspective]
* [http://libguides.lmu.edu/BIOL367 BIOL/CMSI 367 LibGuide]

=== Americans with Disabilities Act—Special Accommodations ===

Students with special needs who require reasonable modifications, special assistance, or accommodations in this course should promptly direct their request to the Disability Support Services (DSS) Office. Any student who currently has a documented disability (ADHD, Autism Spectrum Disorder, Learning, Physical, or Psychiatric) needing academic accommodations should contact the DSS Office (Daum Hall 2nd floor, 310-338-4216) as early in the semester as possible. All discussions will remain confidential. Please visit http://www.lmu.edu/dss for additional information. In addition, please schedule an appointment with the instructor early in the semester to discuss any accommodations for this course for which you have been approved.

=== Health and Wellness ===

There are many resources for student wellness on campus. A central website for students is: http://lionwellness.lmu.edu.

=== Emergency Preparedness ===

To report an emergency or suspicious activity, contact the LMU Department of Public Safety by phone (x222 or 310-338-2894) or at the nearest emergency call box. In the event of an evacuation, follow the evacuation signage throughout the building to the designated safe refuge area where you will receive further instruction from Public Safety or a Building Captain. For more safety information and preparedness tips, visit http://www.lmu.edu/emergency.

=== Revision Notice ===

If necessary, this syllabus and its contents are subject to revision; students are responsible for any changes or modifications announced in class. The most current version of this information resides on this page, the course web site at http://xmlpipedb.cs.lmu.edu/biodb/fall2019/.

Week 11

2019-12-05T21:50:18Z

Kdahlquist: /* Grading for this assignment */ update grading info

'''This journal entry is due on Thursday, November 14, at 12:01am PST.''' ''(Wednesday night/Thursday morning)''

'''''There is an interim deadline for Tuesday, November 12 at 12:01am PST, to decide your team name and select your Project Manager.'''''

'''The [[Week_11#Annotated_Bibliography_.28Due_Tuesday.2C_November_19.2C_12:01am_PST.29 | Annotated Bibliography]] is due Tuesday, November 19 at 12:01am PST.'''

== Overview ==

* Everyone will contribute to [[Week_11#Whole_Team_Journal_Assignment:_Creating_a_Team_Wiki_Page | Creating your team's home page,]] and in the process, getting yourselves organized for the final project.
* To find and use scholarly and discipline-specific professional information, understanding the role of journals and publishers in controlling and providing access to scholarly content.
* To select information that provides relevant evidence on a topic.
* To differentiate between primary and secondary sources, recognizing how their use and importance vary with each discipline.
* To evaluate resources for reliability, validity, accuracy, authority, and bias.
* To prepare and give a journal club presentation, critically evaluating an article from the primary literature.

=== Grading for this assignment ===

* Your individual journal entry with the outline of the journal club paper is worth a total of 10 points.
* Your individual journal entry with annotated bibliography is worth a total of 10 points.
* Your team journal entry for this week is also worth a total of 10 points (instead of 3). Each member of the team will receive the same grade for the team journal entry.
* The journal club presentation (delivered on November 14) will be worth a total of 40 points.

=== Team Membership ===

The project groups and roles are are:
# [[FunGals]]: Kitagawa, E., Takahashi, J., Momose, Y., & Iwahashi, H. (2002). Effects of the pesticide thiuram: genome-wide screening of indicator genes by yeast DNA microarray. Environmental science & technology, 36(18), 3908-3915. DOI: 10.1021/es015705v
#* Project Manager: Mike
#* Quality Assurance: Iliana
#* Data Analysis: Emma, Kaitlyn
#* Coder: Mike
# [[Sulfiknights]]: Thorsen, M., Lagniel, G., Kristiansson, E., Junot, C., Nerman, O., Labarre, J., & Tamás, M. J. (2007). Quantitative transcriptome, proteome, and sulfur metabolite profiling of the Saccharomyces cerevisiae response to arsenite. Physiological genomics, 30(1), 35-43. DOI: 10.1152/physiolgenomics.00236.2006
#* Project Manager: Naomi
#* Quality Assurance: Naomi, Joey
#* Data Analysis: Marcus, Ivy
#* Designer: DeLisa
# [[Skinny Genes]]: Barreto, L., Canadell, D., Valverde‐Saubí, D., Casamayor, A., & Ariño, J. (2012). The short‐term response of yeast to potassium starvation. Environmental microbiology, 14(11), 3026-3042. DOI: 10.1111/j.1462-2920.2012.02887.x
#* Project Manager: Aby
#* Quality Assurance: Christina, Jonar
#* Data Analysis: David, Aby
#* Coder: Mihir

== Individual Journal Assignment ==

* Store this journal entry as "''username'' Week 11" (i.e., this is the text to place between the square brackets when you link to this page).
* Invoke your template on your journal entry page so that you:
** Link from your journal entry page to this Assignment page.
** Link from your journal entry to your user page.
** Add the "Journal Entry" category to the end of your wiki page.
* Because you have invoked your template on your user page, you should also have a:
** Link from your user page to this Assignment page.
** Link to your journal entry from your user page.
* '''''Note that this week, we will add two new categories, "Group Projects" and a category for your team's name.''' Please do not add these to your individual templates because we want these categories to be precisely used for the Group Projects and your team, respectively.''
* Include both the Acknowledgments and References section as specified by the [[Week_1#Academic_Honesty | Week 1]] assignment.
* The sections you need for this week's individual journal entry are Purpose, 10 definitions of terms, Outline of your journal article, Annotated Bibliography, Data/files, Conclusion, Acknowledgments, and References (as specified by the [[Week_1 | Week 1]] assignment).

=== Presentation Prep: Individual Journal Pages ===

In preparation for your journal club presentation, you will each '''''individually''''' complete the following assignment on your individual journal page.

# Make a list of at least 10 biological terms for which you did not know the definitions when you first read the article. Define each of the terms. You can use the glossary in any molecular biology, cell biology, or genetics text book as a source for definitions, or you can use one of many available online biological dictionaries (links below). Cite your sources for the definitions by providing the proper citation (for a book) or the URL to the page with the definition for online sources. '''''Each definition must have it's own citation, to a book or URL.''''' Make an in text citation of the (name, year) format next to the definition, and then list the full citation in the References section of your journal page.
#* Online Biological Dictionaries
#** Web sites
#*** [http://www.biology-online.org/dictionary/Main_Page biology-online.org dictionary]
#*** [http://www.geneontology.org Gene Ontology]
#*** [http://www.cancer.gov/dictionary/ NCI Dictionary of Cancer Terms]
#** eBooks
#*** [http://www.oxfordreference.com/view/10.1093/acref/9780198529170.001.0001/acref-9780198529170 Oxford Dictionary of Biochemistry and Molecular Biology eBook]
#*** [http://www.oxfordreference.com/view/10.1093/acref/9780199204625.001.0001/acref-9780199204625 Dictionary of Biology Oxford Reference eBook]
#*** [http://site.ebrary.com/lib/loyolamarymount/detail.action?docID=10188656 Dictionary of Cell and Molecular Biology eBook]
#*** [http://www.oxfordreference.com/view/10.1093/acref/9780199766444.001.0001/acref-9780199766444 Dictionary of Genetics Oxford Reference eBook]
# Write an outline of the article. The length should be the equivalent of 2-3 pages of standard 8 1/2 by 11 inch paper (you can use the "Print Preview" function in your browser to judge the length). Your outline can be in any form you choose, but you should utilize the wiki syntax of headers and either numbered or bulleted lists to create it. The text of the outline does not have to be complete sentences, but it should answer the questions listed below and have enough information so that others can follow it. However, your outline should be in YOUR OWN WORDS, not copied straight from the article. It is not acceptable to copy another student's outline either. Even if you work together to understand the article, your individual entries need to be in your own words.
## What is the main result presented in this paper?
## What is the importance or significance of this work?
## What were the limitations in previous studies that led them to perform this work?
## How did they treat the yeast cells (what experiment were they doing?)
## What strain(s) of yeast did they use? Were the strain(s) haploid or diploid?
## What media did they grow them in? What temperature? What type of incubator? For how long?
## What controls did they use?
## How many replicates did they perform per treatment or timepoint?
## What method did they use to prepare the RNA, label it and hybridize it to the microarray?
## What mathematical/statistical method did they use to analyze the data?
## Are the data publicly available for download? From which web site?
##Briefly state the result shown in each of the figures and tables, not just the ones you are presenting.
##* What do the X and Y axes represent?
##* How were the measurements made?
##* What trends are shown by the plots and what conclusions can you draw from the data?
## How does this work compare with previous studies?
## What are the important implications of this work?
## What future directions should the authors take?
## Give a critical evaluation of how well you think the authors supported their conclusions with the data they showed. Are there any major flaws to the paper?

=== Journal Club Presentation ===

Each team will prepare and give a 20-25 minute PowerPoint presentation for their paper in class on Thursday, November 14.
* Please follow the [http://brightspace.lmu.edu Presentation Guidelines PowerPoint on Brightspace] for how to format your slides.
* You will need to prepare ~20 slides (assume 1 slide per minute of presentation).
* You need to present the information in the outline of your journal article listed above, but organized as a presentation. You will need the following:
** Title slide with the complete reference to the article, team name, team members, date and "BIOL 367".
** Outline slide (list of key ''messages'' for the talk)
** One or more introduction slides
** A slide for each figure or table from the paper. Figures with multiple parts can/should be split between multiple slides.
** A summary/conclusion slide that is a reflection of the outline.
** Acknowledgments slide
** References slide
* ''Your PowerPoint slides must be uploaded to the wiki and linked to from your '''individual''' journal page '''and''' your '''team''' page by 12:01am on Thursday, November 14'''''
** You can update your slides before your presentation, but I will be grading the ones you upload by the deadline.
* Your presentation (both the slides and the oral presentation) will be evaluated by the instructor using the [[Presentation Rubric]].
* Your presentation will also be evaluated by your fellow classmates (anonymously) who will answer the following questions:
*# What is the speaker's take-home message (one short sentence)?
*# What is the best thing about this presentation?
*# What needs improvement?
*# Please comment on the speaking style (language and delivery) of each presenter.
* Although you may be working with different partners on this presentation than before, you are expected to take the feedback from your previous presentation into account when doing this one.

=== Annotated Bibliography (Due Tuesday, November 19, 12:01am PST)===

This exercise will be primarily performed in class on Tuesday, November 12. What is not finished in class is due on Tuesday, November 19, 12:01am PST).

The purpose of this exercise is to annotate a bibliography of papers related to your assigned paper, that report microarray data from yeast subjected to a timecourse treatment. You will also use these articles to write the Introduction and Discussion sections of your final group report. You will complete the bibliography on your individual journal entry, and summarize it on your team's page.

==== Resources ====

* [http://libguides.lmu.edu/BIOL367 BIOL/CMSI 367 LibGuide]
* [http://scholar.google.com/ Google Scholar]
* [http://www.ncbi.nlm.nih.gov/sites/entrez?holding=calmulib PubMed (LMU-specific link]
* [http://electra.lmu.edu:2048/login?url=http://webofknowledge.com/WOS Web of Science (LMU pays subscription fee for access)]
* [http://www.yeastgenome.org SGD]

==== Assignment ====

* Create a bibliography of a minimum of 8-10 citations to primary research articles related to your assigned article (2 per group member).
** On your team's page, only include the APA-formatted citations in a list for the 8 or 10 articles; the rest of the information below goes on your individual pages.
** Each of the 8-10 references in your bibliography needs to have the following information (an example is given in the section below):
**# The complete bibliographic reference in the APA style (see [https://owl.purdue.edu/owl/research_and_citation/apa_style/apa_formatting_and_style_guide/reference_list_basic_rules.html this link]), ''with DOI''.
**# The link to the abstract from PubMed.
**# The link to the full text of the article in PubMedCentral (if not available, write N/A).
**# The link to the full text of the article (HTML format) from the publisher web site.
**# The link to the full PDF version of the article from the publisher web site.
**# Who owns the rights to the article? Look at the first page of the PDF version of the article for the © symbol. Generally, either the journal/publisher or the authors will hold the copyright.
**# How is the article available to you?
**#* Is the article available “Open Access” (look for the words “open access” or the “unlocked” icon on the article website or the first page of the PDF) If '''YES''', stop here.
**#* If the article is not Open Access, is it available for free after a certain period of time has elapsed? You would not find the words “open access” or the “unlocked” icon, but you would still be able to access the article. If '''YES''', stop here.
**#* Did LMU buy a subscription or pay a fee for your access to this article? You might see “Loyola Marymount University” or “LMU” on the article website. Alternately, a list of the journals that LMU pays for can be found at: [http://sq4ya5rf2q.search.serialssolutions.com/ http://sq4ya5rf2q.search.serialssolutions.com/] If '''YES''', stop here.
**#* Is the article behind a paywall or “subscription-only”? Your attempts to access it when on the LMU network have failed. In this case, if you needed the article, you would use the ILLIAD system to request it by logging in here: [https://lmu.illiad.oclc.org/illiad/illiad.dll?Action=99 https://lmu.illiad.oclc.org/illiad/illiad.dll?Action=99]. ''Note that you don’t need to actually request it for this assignment''.
**# Is the article available online-only or both in print and online? Look on the journal website for a “subscription” link. If that page talks about subscribing to the print edition, then it is available in print. If not, it is available online-only.
**# Who is the publisher of the journal?
**#* Is the publisher for-profit or non-profit?
**#* Is the publisher a scientific society (some scientific societies partner with a for-profit publisher, some act as their own non-profit publisher)
**#* Does the publisher belong to the [http://oaspa.org/membership/members Open Access Publishers Association]?
**#* What country is the journal published in?
**# How Long has the journal been in operaion? (e.g., browse the archive for the earliest article published)
**# Are the articles in this journal peer-reviewed?
**# Provide a link to the scientific advisory board/editorial board of the journal.
**# What is the journal impact factor? (Look to see if it is provided on the journal home page.)
**# Is the article a review or primary research article?
**# Are the data associated with this article available? If so, provide a link to the dataset.
* You must use these three databases/tools to find the references that you include in your bibliography: PubMed, GoogleScholar, and Web of Science. Answer the following questions as part of your assignment:
*# Use a keyword search for the first three databases/tools and answer the following:
*#* PubMed
*#** What original keyword(s) did you use? How many results did you get?
*#** Which terms in which combinations were most useful to narrow down the search? How many results did you get after narrowing the search?
*#* Google Scholar
*#** What original keyword(s) did you use? How many results did you get?
*#** Which terms in which combinations were most useful to narrow down the search? How many results did you get after narrowing the search?
*#* Web of Science
*#** What original keyword(s) did you use? How many results did you get?
*#** Which terms in which combinations were most useful to narrow down the search? How many results did you get after narrowing the search?
*# Use the advanced search functions for each of these three databases/tools and answer the following:
*#* PubMed
*#** Which advanced search functions were most useful to narrow down the search? How many results did you get?
*#* Google Scholar
*#** Which advanced search functions were most useful to narrow down the search? How many results did you get?
*#* Web of Science
*#** Which advanced search functions were most useful to narrow down the search? How many results did you get?
*#* Perform a prospective search on your article in the Web of Science and answer the following:
*#** How many articles does this article cite?
*#** How many articles cite this article?
* Reflect:
*# What impact does the choice of keywords have on your results?
*# What are the advantages and disadvantages of each of the three databases (Pubmed, Google Scholar, Web of Science)?

==== Sample Bibliographic Entry ====

For example, see the bibliographic entry for Schade et al. (2004) below which is available both in print and online:

# Schade, B., Jansen, G., Whiteway, M., Entian, K.D., & Thomas, D.Y. (2004). Cold Adaptation in Budding Yeast. ''Molecular Biology of the Cell'', 15, 5492-5502. doi: 10.1091/mbc.E04-03-0167
# PubMed Abstract: http://www.ncbi.nlm.nih.gov/pubmed/15483057
# PubMed Central: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC532028/
# Publisher Full Text (HTML): http://www.molbiolcell.org/content/15/12/5492.long
# Publisher Full Text (PDF): http://www.molbiolcell.org/content/15/12/5492.full.pdf+html
# Copyright: 2004 by the American Society for Cell Biology (information found on PDF version of article);
# Article is not Open Access, but is freely available 2 months after publication, published in the United States, LMU did not pay for the article
# Availability: in print and online
# Publisher: American Society for Cell Biology (scientific society), non-profit, not a member of the OAPA
# The journal begain in 1989
# Peer-reviewed articles: yest
# [https://www.molbiolcell.org/editorial-board Editorial board].
# Impact factor: 3.512 (2017)
# Primary research article
# Data are not available (dead hyperlink)

== Whole Team Journal Assignment: Creating a Team Wiki Page ==

From this week on, your "Shared Journal Assignments" will become "Team Journal Assignments". For this week, some preliminary tasks are assigned to your team to kickstart your final projects.
# Select your team's Project Manager.
#* Add the name of your project manager to the [[Project Manager]] guild page and [[Final Project | Overview]] pages.
# Name your team and create your team home page on the wiki.
#* The name of your team home page should simply be the team name.
#* This page will be the main place from which your team project will be managed. Include all of the information/links that you think will be useful for your team to organize your work and communicate with each other and with the instructors. ''Hint: the kinds of things that are on your own User pages and on the course Main page can be used as a guide.''
# Create a link to your team's page on the course Main page.
# Create a template for your team with useful information and links that you will invoke on all pages that you will create for the project.
#* Create a category using your team name and include it on your team's template so that it also gets used on all pages you will create for the project. Also use include the category "Group Projects" in your template.
#** ''However, please do not add these categories to your own individual templates because we want them to precisely mark pages having to do with the Group Projects and your team, respectively.''
# Each person needs to write a short executive summary of that person's progress on the project for the week, with links to the relevant individual journal pages (which will have more detailed information).
# Each team member should reflect on the team's progress:
## What worked?
## What didn't work?
## What will I do next to fix what didn't work?
# Note that you will be directed to add specific information to your team's pages in the individual portion of the assignment for this and future weeks.

[[Category:Group Projects]]

[[Category:Assignment]]

Main Page

2019-12-04T18:46:43Z

Kdahlquist: /* Course Work and Grading */ updated point totals

__NOTOC__
<div style="text-align: center; font-size: 175%">Loyola Marymount University</div>
<div style="text-align: center; font-size: 250%; line-height: 1.25em">'''BIOL 367-01: Biological Databases'''</div>
<div style="text-align: center; font-size: 175%; margin-bottom: 0.85em">Fall 2019</div>

<div class="toc-container">__TOC__</div>

== Announcements ==

* Please complete the course evaluation on [http://brightspace.lmu.edu Brightspace] by class time on Thursday.
* Data Analysts and QA's who have a partner in their group can have a shared ''individual'' journal entry. Both students will be given the same grade and are expected to contribute equally to the electronic lab notebook.

* '''The combined database may be found [[Media:BIOL 478 - BioDB CombinedDatabase.zip|here]].'''

=== Upcoming Seminars ===

* '''LMU Society of Physics Students:''' Wednesday, December 4, 4:30 - 6:00 pm, Von der Ahe 190, Dr. Brian Keating, author of ''Losing the Nobel Prize'' will engage students with his research on imaging the cosmic background radiation, a remnant of the Big Bang.

== Instructor ==
* [[User:Kdahlquist|Kam D. Dahlquist]]

== Students ==

{| style="margin-left: auto; margin-right: auto"
| style="padding-right: 1em" | [[User:Marmas|Michael Armas]]
| style="padding-right: 1em" | [[User:Cdomin12|Christina Dominguez]]
| style="padding-right: 1em" | [[User:knguye66|Kaitlyn Nguyen]]
| style="padding-right: 1em" | [[User:Ntesfaio|Naomi Tesfaiohannes]]
|-
| style="padding-right: 1em" | [[User:mavila9|Marcus Avila]]
| style="padding-right: 1em" | [[User:Imacarae|Ivy-Quynh Macaraeg]]
| style="padding-right: 1em" | [[User:Jnimmers| John Nimmers-Minor]]
| style="padding-right: 1em" | [[User:eyoung20|Emma Young]]
|-
| style="padding-right: 1em" | [[User:Jcowan4|Jonar Cowan]]
| style="padding-right: 1em" | [[User:Dmadere | DeLisa Madere]]
| style="padding-right: 1em" | [[User:dramir36| David Ramirez]]
| style="padding-right: 1em" |
|-
| style="padding-right: 1em" | [[User:Icrespin| Iliana Crespin]]
| style="padding-right: 1em" | [[User:ymesfin| Yeabsira Mesfin]]
| style="padding-right: 1em" | [[User:msamdars| Mihir Samdarshi]]
| style="padding-right: 1em" |
|}

{{Final Project Links}}

== Course Schedule ==

The current course schedule is shown below. In addition to the listed in-class agendas, the following guidelines are also notable:

* Unless otherwise stated on the schedule, your weekly journals/assignments are due on Thursdays at 12:01am. In cases where subsequent revisions or corrections will be accepted, the instructor will provide feedback and submission deadlines on a per-assignment basis.
* Reading assignments should be completed ''before'' coming to class.

=== Part 1: Building Blocks (Genetic Code and Manipulating Text) ===

{| class="schedule-table"
! #
! Date
! Reading
! Agenda
! Journal
|-
! 1
| Tuesday<br/>8/27/2019
|
|
* Syllabus walkthrough
* Pairwise interviews and introductions
* Academic Honesty case studies
* Class wiki account: Set up a username and password for this wiki site
| rowspan="2" | [[Week 1]]
(due at 12:01am 9/5)

[[Class_Journal_Week_1 | Class Journal Week 1]]
|-
!
| Thursday<br/>8/29/2019
| [http://brightspace.lmu.edu Chapter 1 of ''On Becoming a Biologist''] by John Janovy, Jr. (on Brightspace)

[http://dl.acm.org/ft_gateway.cfm?id=1378711 ''Voices of Computing''] and [http://dl.acm.org/ft_gateway.cfm?id=1272529 ''Computing is a Natural Science''] by Peter Denning (these links should be accessed from within LMU to get the full article)

|
* What is a model?
** Model of a cell/model of a computer

** [http://brightspace.lmu.edu Slides on Brightspace]

{{Course Schedule Divider}}
|-
! 2
| Tuesday<br/>9/3/2019
|
|
* Discuss the [[Week 1]] assignment
** Wiki syntax overview
** Lab session to setup User wiki page

| rowspan="2" | [[Week 2]]
(due at 12:01am 9/12)

[[Class Journal Week 2]]

|-
!
| Thursday<br/>9/5/2019
|
* [http://www.ncbi.nlm.nih.gov/books/NBK21134/#A5234 Ch. 1 ''Genomes 2'']
* [http://www.ncbi.nlm.nih.gov/books/NBK21121/ Brown, T.A. (2002) ''Genomes 2'', Ch. 3, especially section 3.3.2]
* On [http://brightspace.lmu.edu Brightspace]:
** Nirenberg (2004) ''Deciphering the Genetic Code''
** Kaji & Kaji (2004) ''Setting the Record Straight''
** Moody (2004) ''Digital Code of Life'', Ch. 1
** Hayes (2004) ''Ode to the Code''

|
* DNA structure
** [http://brightspace.lmu.edu Slides on Brightspace]

{{Course Schedule Divider}}
|-
! 3
| Tuesday<br/>9/10/2019
|
|
* Aipotu work session

| rowspan="2" | [[Week 3]]
(due at 12:01am 9/19)

[[Class Journal Week 3]]

|-
!
| Thursday<br/>9/12/2019
|
|
* Genes as models
* Central model of molecular biology
** [http://www.youtube.com/watch?v=u9dhO0iCLww Berkeley translation video]
** This [http://www.dnai.org/a/index.html web site] has links to animations of the replication, transcription, and translation processes.
** [http://brightspace.lmu.edu Slides on Brightspace]
* [[Media:BIOL367_Fall2019_GeneticCode-CentralModel-2.pdf | Paper and pencil exercise]]

{{Course Schedule Divider}}
|-
! 4
| Tuesday<br/>9/17/2019
|
* [http://www.bloomberg.com/graphics/2015-paul-ford-what-is-code/ Ford, Paul. “What is Code?” ''Business Week'', June 11, 2015.]
* [https://www.dataone.org/sites/all/documents/education-modules/pptx/L01_DataManagement.pptx DataONE: Data Management Slides]
|
Introduction to biological databases (part 1)
* What is bioinformatics?
* Open Science Ecosystem
* DataONE: Data Management and the life cycle of data
* Slides are on [http://brightspace.lmu.edu Brightspace]

| rowspan="2" | [[Week 4]]
(due at 12:01am 9/26)

[[Class Journal Week 4]]

|-
!
| Thursday<br/>9/19/2019
|
| Introduction to biological databases (part 2)
* [http://dondi.lmu.build/share/intro/wheres-my-stuff.pdf Where's my Stuff?]
* Slides are on [http://brightspace.lmu.edu Brightspace]
* An overview of computers, networks, files, web applications, web services, and databases
* Names, URLs, identifiers
** As applied to biology: genes, species
* “Stuff” on the worldwide web
** Accessing the web on a browser

{{Course Schedule Divider}}
|-
! 5
| Tuesday<br/>9/24/2019
|
|
[[ Week 4]] work session
| rowspan="2" | [[Week 5]]
(individual due at 12:01am 10/1)

'''''Note Tuesday Due Date'''''

[[Class Journal Week 5]]

(shared due at 12:01am 10/3)

|-
!
| Thursday<br/>9/26/2019
| [https://academic.oup.com/nar/article/47/D1/D1/5280358 The 26th annual Nucleic Acids Research database issue]

[http://www.oxfordjournals.org/nar/database/a/ The NAR Molecular Biology Database Collection]

|
Introduction to biological databases (part 2)
* Terminology, types, evaluation, four "Gold Standard" databases
* A closer look at evaluating biological databases (slides on [http://brightspace.lmu.edu Brightspace])
* [http://www.dataone.org/sites/all/documents/L02_DataSharing.pptx DataONE: Data Sharing]
* [http://brightspace.lmu.edu Presentation Guidelines PowerPoint on Brightspace]

{{Course Schedule Divider}}
|-
! 6
| Tuesday<br/>10/1/2019
|
| Database presentations part 1
# group 1
# group 2
# group 3
# group 4
| rowspan="2" | [[Week 6]]
(due at 12:01am 10/10)

[[Class Journal Week 6]]
|-
!
| Thursday<br/>10/3/2019
|
| Database presentations part 2
# group 1
# group 2
# group 3

[[Presentation Rubric]]

[http://brightspace.lmu.edu/ Presentation Guidelines on Brightspace]

|}

=== Part 2: Going Deeper (Gene Expression Data and Relational Databases) ===

{| class="schedule-table"
! #
! Date
! Reading
! Agenda
! Journal
|-
! 7
| Tuesday<br/>10/8/2019
| For more on the Human Genome Project, see:
* Moody (2004) Chapter 6 (on [http://brightspace.lmu.edu Brightspace])
* [http://www.nature.com/news/2010/100623/pdf/4651000a.pdf Science After the Sequence]
* [http://www.nature.com/news/2010/100623/full/4651000a.html Nature Special: Human Genome at 10]
* [http://www.sciencemag.org/content/vol291/issue5507/index.dtl Human Genome Project articles in ''Science'']
* [http://www.nature.com/nature/journal/v409/n6822/index.html Human Genome Project articles in ''Nature'']
|
* Human Genome Project to DNA microarrays (slides on [http://brightspace.lmu.edu Brightspace])
| rowspan="2" | [[Week 7]]
(due at 12:01am 10/17)

[[Class Journal Week 7]]

|-
!
| Thursday<br/>10/10/2019
| [http://www.ncbi.nlm.nih.gov/books/NBK26818/#A1633 Alberts ''et al''. (2002) ''Molecular Biology of the Cell'', Ch. 8: Microarrays]

[http://www.nature.com/ng/journal/v21/n1s/full/ng0199supp_33.html Brown & Botstein (1999) "Microarrays" ''Nature Genetics'']

Campbell & Heyer Chapter 4 (on [http://brightspace.lmu.edu on Brightspace])

DeRisi et al. (1997) [http://www.sciencemag.org/content/278/5338/680.full ''Science'' 278: 680-686.]
| Review [[Week 6]] assignment

{{Course Schedule Divider}}
|-
! 8
| Tuesday<br/>10/15/2019
| [http://www.dataone.org/sites/all/documents/L04_DataEntryManipulation.pptx DataONE: Data Entry and Manipulation]
| More about DNA microarray analysis
* Slides on [http://brightspace.lmu.edu Brightspace]
| rowspan="2" | [[Week 8]]
(due at 12:01am 10/24)

[[Class Journal Week 8]]
|-
!
| Thursday<br/>10/17/2019
|
| Begin DNA microarray analysis part 1
* Statistical analysis of Dahlquist Lab yeast cold shock dataset
* Test your understanding: [http://xkcd.com/882/ http://xkcd.com/882/]

{{Course Schedule Divider}}
|-
! 9
| Tuesday<br/>10/22/2019
| [https://academic.oup.com/bioinformatics/article/21/suppl_1/i159/203147 Ernst & Bar-Joseph (2006) STEM: a tool for the analysis of short time series gene expression data.]
|
Microarray Data Analysis part 2
* Clustering with stem software



| rowspan="2" | [[Week 9]]
(due at 12:01am 10/31)

[[Class Journal Week 9]]

|-
!
| Thursday<br/>10/24/2019
|
* [https://academic.oup.com/nar/article/46/D1/D348/4191333 Teixeira, et al. (2017). YEASTRACT: an upgraded database for the analysis of transcription regulatory networks in ''Saccharomyces cerevisiae'']
* [https://peerj.com/articles/cs-85/ Dahlquist et al. (2016) GRNsight: a web application and service for visualizing models of small- to medium-scale gene regulatory networks]
|
Microarray Data Analysis part 3
* Finding a candidate GRN ([http://www.yeastract.com/ YEASTRACT])

{{Course Schedule Divider}}
|-
! 10
| Tuesday<br/>10/29/2019
| [https://link.springer.com/article/10.1007/s11538-015-0092-6 Dahlquist et al. (2015) Parameter Estimation for Gene Regulatory Networks from Microarray Data: Cold Shock Response in ''Saccharomyces cerevisiae'']
|
Microarray Data Analysis part 4
* Visualizing the candidate GRN ([https://dondi.github.io/GRNsight/ GRNsight])
* Dynamical systems modeling of the gene regulatory network ([http://kdahlquist.github.io/GRNmap/ GRNmap])

| rowspan="2" | [[Week 10]]
(due at 12:01am 11/7)

[[Class Journal Week 10]]
|-
!
| Thursday<br/>10/31/2019
|
|
Microarray Data Analysis part 5
* Visualizing and interpreting the modeling results ([https://dondi.github.io/GRNsight/ GRNsight])
* Group requests due in class (collected on notecards)
|}

=== Part 3: Integrating for Research (GRNsight Gene Expression Database Project) ===

{| class="schedule-table"
! #
! Date
! Reading
! Agenda
! Journal
|-
! 11
| Tuesday<br/>11/5/2019
|
| Dynamical Systems Modeling with GRNmap
| rowspan="2" | [[Week 11]]

(due at 12:01am 11/14)

'''''Thursday Due Date'''''
|-
!
| Thursday<br/>11/7/2019
|
|
Begin GRNsight Expression Database Project
* Team assignments
* Overview of project
* Work session to create Team wiki pages

{{Course Schedule Divider}}
|-
! 12
| Tuesday<br/>11/12/2019
|
|
Literature searching and annotated bibliographies
* ''Course LibGuide:'' http://libguides.lmu.edu/BIOL367
* Select microarray papers for journal club

| rowspan="2" | [[Week 12/13]]

(due at 12:01am 11/26)

'''''Note Tuesday Due Date'''''

|-
!
| Thursday<br/>11/14/2019
|
| Journal Club Presentations

[[Presentation Rubric]]

[http://brightspace.lmu.edu Presentation Guidelines PowerPoint on Brightspace]

{{Course Schedule Divider}}
|-
! 13
| Tuesday<br/>11/19/2019
|
|
* Status report
* Work session

| rowspan="2" | [[Week 12/13]]

(due at 12:01am 11/26)<br/>
'''''Note Tuesday due date'''''
|-
!
| Thursday<br/>11/21/2019
|
|
* Status report
* Work session

|-
! 14
| Tuesday<br/>11/26/2017
|
|
* Status report
* Work session
| '''''No Week 14 assignment'''''
|-
! style="background: #ded" |
| style="background: #efe; border: none" | Thursday<br/>11/28/2019
| style="background: #efe; border: none" |
| style="background: #efe; border: none" | Thanksgiving—no class
| style="background: #efe; border: none" |
{{Course Schedule Divider}}
|-
! 15
| Tuesday<br/>12/3/2019
|
|
* Status report
* Work session
| rowspan="2" | [[Week 15]]

(due at 4:00pm 12/13)
|-
!
| Thursday<br/>12/5/2019
|
|
* Status report
* Work session
{{Course Schedule Divider}}
|-
! style="background: #edd" | F
| style="background: #fee; border: none" | Tuesday<br/>12/10/2019
| style="background: #fee; border: none" |
| style="background: #fee; border: none" | '''Final project presentations 2:00-4:00 PM'''
| style="background: #fee; border: none" |
|-
!
| Friday<br/>12/13/2019 4:00 PM
|
| '''Project deliverables due 4:00 PM'''
|
|}

== Course Information ==

=== Instructor ===

==== [[User:Kdahlquist | Kam D. Dahlquist, Ph.D.]] ====
: http://kdahlquist.github.io/DahlquistLab/index.htm
: '''Phone:''' (310) 338-7697
: '''E-mail:''' kdahlquist at lmu dot edu
: '''Office:''' Life Sciences Building 289
: '''Office Hours (Fall 2019):''' Mondays & Wednesdays 2:00-3:00 PM, Tuesdays & Thursdays 4:00-5:00 PM, and by appointment; I keep a sign-up sheet next to my office door to facilitate appointment-making.

=== Prerequisites/Recommended Background ===

To take this course, you must have upper division standing in the Seaver College of Science and Engineering. Otherwise, there are no strict prerequisites. Backgrounds in biology and computer science, as well as prior experience with web, database, or information management applications, may be helpful but not necessary.

=== Class Meetings and Attendance ===
TR 2:40–3:55pm, Seaver 120 

This is a hands-on, participatory course, thus attendance at all class meetings is required. Each student is allowed two “sick” days (automatically excused absences) during the semester. Further unexcused absences from class will result in a 5% deduction from the overall course grade for each absence. Every effort should be made to attend class on oral presentation days as the content of that day's class is dependent on student participation. Unexcused absences from an oral presentation will result in a grade of zero for the presentation. The instructor should be notified as soon as possible, electronically or by phone, of the reasons for all absences.

Note that the last day to add or drop a class without a grade of W is August 30. The withdrawal or credit/no-credit status deadline is November 1.

=== Mutual Responsibilities ===

This course is designed to foster your development as a biologist and computer scientist and to give you an authentic research experience. We will be engaged together in discovering, examining, and practicing the personal qualities, technical skills, and community standards of the scientific community. While you are ultimately responsible for your own learning, you are not alone. Our class constitutes a team where we will be learning from each other. The role of the instructor is to provide the expert coaching to support and assist you on your journey. All of the exercises, readings, assignments, and policies detailed below have been designed with this purpose in mind.

=== Classroom Conduct ===

We are all responsible for maintaining a classroom and laboratory environment that is safe and conducive to learning. As such, we will observe the following:

# As an LMU Lion, by the [http://studentaffairs.lmu.edu/wellness/lmucares/aboutlmucares/livethelionscode/ Lion’s code], you are pledged to join the discourse of the academy with honesty of voice and integrity of scholarship and to show respect for staff, professors, and other students.
# You are responsible for your own learning and for being a good class citizen.
# Class will start promptly on time.
# You are expected to come to class having done the assigned reading and preparatory work so that you are ready to participate in discussions and to perform the laboratory exercises.
# You are expected to bring the required materials to each class session.
# Cell phones and other ancillary devices must be turned off and put away out of sight. Your own laptop and/or tablet may be used to conduct the class exercises, '''''provided that you have confirmed with the instructor that you have the correct versions of software installed for the exercise'''''. If, however, the laptops/tablets are being used for other purposes and become distracting to you or others, you will be asked to put them away.
# All students are governed by [https://studentaffairs.lmu.edu/media/studentaffairs/osccr/documents/1920-LMU-community-standards.pdf LMU Community Standards publication]. Disruptive behavior which is persistent or significantly interferes with classroom activities may be subject to disciplinary action. A student may be referred to the Office of Student Judicial Affairs if his or her behavior constitutes a violation of the conduct code.

=== Course Website ===

This is the course web site and wiki, hosted by http://xmlpipedb.cs.lmu.edu/biodb/fall2019/. You will need to have a user account on this wiki to be able to edit the wiki and complete coursework. Updates to the course schedule and electronic copies of all handouts, assignments, and readings will be posted to this site. You will also use the site to keep an electronic lab notebook/journal for the course. In addition, students have been automatically enrolled in the course page on [http://brightspace.lmu.edu Brightspace]. The site will be used for materials that cannot be made public on this wiki, including grades.

=== Email Communication ===

At times we will communicate with the entire class using campus email systems, so it is essential that you regularly check your lion.lmu.edu email address or forward your lion account email to your preferred email address. Messages sent to the instructor at night or on the weekend will be answered the next school day.

=== Required Materials ===

==== Texts ====

There is no required text to purchase for the course; materials will be put on reserve at Hannon Library or will be available online on this wiki or [http://brightspace.lmu.edu Brightspace] site. Specific reading assignments are given on the course schedule and should be completed before coming to class.
* Assorted handouts, articles, and sample code will be distributed throughout the semester.
* Additional information is also available on the web; do not hesitate to look for further sources of information regarding the concepts, techniques, tools, and paradigms that we will discuss.

==== Materials (must be brought to each class meeting) ====

* 3-ring binder with all course handouts
* Pen, pencil, extra paper
* USB flash drive to store data
* Account for this wiki
* [http://www.github.com GitHub account]

=== Course Description ===

The disciplines of biology and computer science come together in bioinformatics, where computational tools are needed to manage and analyze the flood of data coming from new genomics technologies. Biological databases form a significant part of this young and exciting field. This course introduces students to both the biology and computer science expertise needed to understand, use, and develop biological databases. Biology topics include the fundamentals of genetics, molecular biology, and biochemistry needed to understand the data stored in biological databases, as well as the biotechnologies used to gather these data in a high-throughput manner. Computer science topics include what biological databases are, why they are important (and needed), and the challenges that arise in compiling them effectively. Biology and computer science lectures on topics that are relevant to biological databases are coupled with hands-on experience with a variety of software packages ranging from bioinformatics utilities to general-purpose database and software development tools. After learning how to use a biological database, students will be asked to build one of their own.

==== Course Objectives and Learning Outcomes ====

This course is built upon L. Dee Fink’s ''taxonomy of significant learning'', as applied to biological databases. Long after the course concludes, our hope is that:
* You understand how biological information is encoded in the genome and can apply this knowledge to a variety of biological tasks and problems
* You understand the core concepts, structure, and functions of a database, ranging from individual files to a full relational database management system, and can perform useful tasks with such data
* You show discipline and proficiency in day-to-day science and engineering best practices, such as maintaining journals and notebooks, managing your files and code, and critically evaluating scientific and technical information
* You recognize and care about how the biological and technological issues presented in this course relate to and affect society, our daily lives, and ourselves
* You have some skills and tools for “leaving your comfort zone,” flourishing outside of it, and learning more about biology and computer science on your own
* You learn how to communicate and work effectively with colleagues from different disciplines

==== University Core Curriculum ====

This course fulfills the following requirements in the University Core Curriculum:
* Integrations: Interdisciplinary Connections
* Upper Division Information Literacy Flag
* Upper Division Oral Communication Flag

=== Course Work and Grading ===

Your work in this course will be assessed in the following areas:

{| style="border: 1px dashed rgb(47, 111, 171); background: rgb(249, 249, 249); margin: 1em 0; padding: 1em" cellpadding="5ex"
| Weekly electronic lab notebook/journal assignments, individual (usually 10 points each)
| style="text-align: right" | 155 
| points
|-
| Weekly electronic lab notebook/journal assignments, shared (usually 3 points each)
| style="text-align: right" | 43 
| points
|-
| Oral presentations
| style="text-align: right" | 120
| points 
|-
| Final Project Deliverables (including written report)
| style="text-align: right" | 100
| points
|-
| '''Total'''
| style="text-align: right" | '''418''' 
| '''points'''
|}

Final course grading scale:
94.0-100.0% A
90.0- 93.9% A-
86.0- 89.9% B+
82.0- 85.9% B
78.0- 81.9% B-
74.0- 77.9% C+
70.0- 73.9% C
67.0- 69.9% C-
60.0- 66.9% D
≤ 59.9% F

Current grades will be made available at [http://brightspace.lmu.edu Brightspace].

==== Electronic Laboratory Notebook/Journal ====

One of the most important skills you can develop as a scientist is keeping an excellent laboratory notebook. For computational research, the equivalent of the biology paper-based lab notebook is documentation of your “workflow.” For this course you will practice documentation skills by keeping an electronic lab notebook or journal. The technology we will use is this wiki. We will create and edit during the semester. The wiki software is the same one that runs Wikipedia and many other wiki sites, so proficiency gained here is easily transferable to other applications.

You will create an individual user page and make weekly entries that the instructor will read and grade. You will use the wiki to complete the assignments as well. The following guidelines apply:
* Your weekly journal entry is typically due every 12:01am on Thursday Pacific time; consult the schedule for specific due dates for each assignment.
* Each weekly assignment has an individual component and a shared component. You will earn 10 points per weekly submission for the individual journal entry and 3 points per submission for the shared journal entry. '''''Late journal entries will be accepted up to one week later for up to half credit.'''''
* The instructor will read and comment on how to improve your journal entries on your user talk pages.
* Depending on the type of assignment for that week, you may be given the opportunity to make improvements to previous journal entries as the semester progresses.
* For most weeks in the semester, you will be assigned a "homework partner". You will be expected to consult with your partner in order to complete the assignment. However, unless otherwise stated, each partner must submit his or her own work as the individual journal entry (direct copies of each other's work is not allowed).
* Generally, your journal entries will consist of:
** Your electronic laboratory notebook (workflow and other documentation) for hands-on exercises and projects
** Answers to any specific questions posed in the exercise
** Acknowledgments section (see [[Week 1]] assignment for details)
** References section (see [[Week 1]] assignment for details)
** Shared reflection on your learning, assigned readings, or ethics case studies.

==== Oral Presentations ====

You will give three oral presentations in this course. The first two will be in the format of a “Journal Club” presentation where students will present and lead discussion of research articles from the primary literature. The third will be a research presentation on your final project. '''''Because that day’s class content is dependent upon each student being ready to present and lead discussion, late journal club presentations will not be accepted. An unexcused absence from a journal club presentation will result in a grade of zero for the presentation.'''''

==== Final Group Project ====



In addition to the research presentation, the culmination of your final project will be the preparation of a written laboratory report in the style of a manuscript that could be submitted to a peer-reviewed journal. Specific instructions will be posted later in the semester. '''''The Final Written Report cannot be accepted any later than Friday, December 13 at 4:00pm. The Final Written Report must be completed to receive a passing grade in the course.'''''

==== Extra Credit ====

Students may accumulate up to 12.5 points toward their final grade in extra credit by attending Biology Department seminars and completing the seminar sheets. Each seminar attended is worth 2.5 points with up to 5 seminars (12.5 points) total. You must attend the entire seminar from start to finish and personally turn in your seminar sheet to a faculty member at the end of the seminar.

Certain non-Biology Department seminars may be approved in advance for extra credit at the instructor's discretion. To receive credit for these seminars, you must turn in a one-page '''''hard copy''''' of your summary of the seminar '''''in class, within one week''''' of the date of the seminar or they will not count as extra credit.

=== Work Load Expectations ===

In line with [http://www.lmu.edu/Assets/LMU+Credit+Hour+Policy_Final.pdf LMU’s Credit Hour Policy], the work load expectation for this course is that for every one hour (50 minutes) of classroom instruction, you will complete a minimum of two hours of out-of-class student work each week. This is a 3-unit course with 3 hours (150 minutes) of instruction per week. Thus the expectation is that you will complete 6 hours of work outside of class per week.

=== University Policy on Academic Honesty ===

Loyola Marymount University is a community dedicated to academic excellence. Academic honesty in scholarship and creative work stands at the center of LMU's academic life, and is essential for true learning and creation of knowledge to take place. As a university in the Jesuit and Marymount traditions, this community expects its members to act in accordance with the highest standards of honesty and ethics at all times. Violations of academic honesty undermine the fundamental educational mission of the University and cannot be tolerated.

Academic dishonesty will be treated as an extremely serious matter with severe consequences. '''''The minimum penalty for an instance of academic dishonesty in this course, even on a 1-point assignment or extra credit assignment, is a one-letter grade penalty in the course and a zero on the assignment.''''' It is never permissible to turn in any work that has not been authored by the student, such as work that has been copied from another student or copied from a source (including Internet) without properly acknowledging the source. It is the student's responsibility to make sure that your work meets the standard set forth in the “Academic Honesty Policy” (see http://academics.lmu.edu/honesty.) You are responsible for contacting the instructor before assignments are due to proactively resolve any questions you may have.

You are required to sign the [[Media:BIOL367_Fall2019_AcademicHonestyAgreement.pdf|Academic Honesty Agreement]] for this course.

==== Academic Honesty Resources ====
* [[Media:BIOL367_Fall2019_AcademicHonestyAgreement.pdf|Academic Honesty Agreement]]
* [http://academics.lmu.edu/media/lmuacademics/academichonesty/documents/Academic%20Honesty%20Policy%20FINAL%20Appendices--051116.pdf LMU Academic Honesty Policy and Procedures]
* [http://www.bio.davidson.edu/dept/plagiarism.html The Davidson College Department of Biology Statement on Plagiarism]
* [[Media:BIOL367_Fall2019_GuidelinesforLiteratureCitations.pdf | Guidelines for Literature Citations in a Scientific Paper]]
** [https://owl.purdue.edu/owl/research_and_citation/apa_style/apa_formatting_and_style_guide/reference_list_basic_rules.html APA Style for References]
* [http://dondi.lmu.build/share/misc/plagiarism.pdf Notes on plagiarism from an electrical engineering & computer science perspective]
* [http://libguides.lmu.edu/BIOL367 BIOL/CMSI 367 LibGuide]

=== Americans with Disabilities Act—Special Accommodations ===

Students with special needs who require reasonable modifications, special assistance, or accommodations in this course should promptly direct their request to the Disability Support Services (DSS) Office. Any student who currently has a documented disability (ADHD, Autism Spectrum Disorder, Learning, Physical, or Psychiatric) needing academic accommodations should contact the DSS Office (Daum Hall 2nd floor, 310-338-4216) as early in the semester as possible. All discussions will remain confidential. Please visit http://www.lmu.edu/dss for additional information. In addition, please schedule an appointment with the instructor early in the semester to discuss any accommodations for this course for which you have been approved.

=== Health and Wellness ===

There are many resources for student wellness on campus. A central website for students is: http://lionwellness.lmu.edu.

=== Emergency Preparedness ===

To report an emergency or suspicious activity, contact the LMU Department of Public Safety by phone (x222 or 310-338-2894) or at the nearest emergency call box. In the event of an evacuation, follow the evacuation signage throughout the building to the designated safe refuge area where you will receive further instruction from Public Safety or a Building Captain. For more safety information and preparedness tips, visit http://www.lmu.edu/emergency.

=== Revision Notice ===

If necessary, this syllabus and its contents are subject to revision; students are responsible for any changes or modifications announced in class. The most current version of this information resides on this page, the course web site at http://xmlpipedb.cs.lmu.edu/biodb/fall2019/.

Main Page

2019-12-03T23:24:13Z

Kdahlquist: /* Announcements */ add announcement about course evaluation

__NOTOC__
<div style="text-align: center; font-size: 175%">Loyola Marymount University</div>
<div style="text-align: center; font-size: 250%; line-height: 1.25em">'''BIOL 367-01: Biological Databases'''</div>
<div style="text-align: center; font-size: 175%; margin-bottom: 0.85em">Fall 2019</div>

<div class="toc-container">__TOC__</div>

== Announcements ==

* Please complete the course evaluation on [http://brightspace.lmu.edu Brightspace] by class time on Thursday.
* Data Analysts and QA's who have a partner in their group can have a shared ''individual'' journal entry. Both students will be given the same grade and are expected to contribute equally to the electronic lab notebook.

=== Upcoming Seminars ===

* '''LMU Society of Physics Students:''' Wednesday, December 4, 4:30 - 6:00 pm, Von der Ahe 190, Dr. Brian Keating, author of ''Losing the Nobel Prize'' will engage students with his research on imaging the cosmic background radiation, a remnant of the Big Bang.

== Instructor ==
* [[User:Kdahlquist|Kam D. Dahlquist]]

== Students ==

{| style="margin-left: auto; margin-right: auto"
| style="padding-right: 1em" | [[User:Marmas|Michael Armas]]
| style="padding-right: 1em" | [[User:Cdomin12|Christina Dominguez]]
| style="padding-right: 1em" | [[User:knguye66|Kaitlyn Nguyen]]
| style="padding-right: 1em" | [[User:Ntesfaio|Naomi Tesfaiohannes]]
|-
| style="padding-right: 1em" | [[User:mavila9|Marcus Avila]]
| style="padding-right: 1em" | [[User:Imacarae|Ivy-Quynh Macaraeg]]
| style="padding-right: 1em" | [[User:Jnimmers| John Nimmers-Minor]]
| style="padding-right: 1em" | [[User:eyoung20|Emma Young]]
|-
| style="padding-right: 1em" | [[User:Jcowan4|Jonar Cowan]]
| style="padding-right: 1em" | [[User:Dmadere | DeLisa Madere]]
| style="padding-right: 1em" | [[User:dramir36| David Ramirez]]
| style="padding-right: 1em" |
|-
| style="padding-right: 1em" | [[User:Icrespin| Iliana Crespin]]
| style="padding-right: 1em" | [[User:ymesfin| Yeabsira Mesfin]]
| style="padding-right: 1em" | [[User:msamdars| Mihir Samdarshi]]
| style="padding-right: 1em" |
|}

{{Final Project Links}}

== Course Schedule ==

The current course schedule is shown below. In addition to the listed in-class agendas, the following guidelines are also notable:

* Unless otherwise stated on the schedule, your weekly journals/assignments are due on Thursdays at 12:01am. In cases where subsequent revisions or corrections will be accepted, the instructor will provide feedback and submission deadlines on a per-assignment basis.
* Reading assignments should be completed ''before'' coming to class.

=== Part 1: Building Blocks (Genetic Code and Manipulating Text) ===

{| class="schedule-table"
! #
! Date
! Reading
! Agenda
! Journal
|-
! 1
| Tuesday<br/>8/27/2019
|
|
* Syllabus walkthrough
* Pairwise interviews and introductions
* Academic Honesty case studies
* Class wiki account: Set up a username and password for this wiki site
| rowspan="2" | [[Week 1]]
(due at 12:01am 9/5)

[[Class_Journal_Week_1 | Class Journal Week 1]]
|-
!
| Thursday<br/>8/29/2019
| [http://brightspace.lmu.edu Chapter 1 of ''On Becoming a Biologist''] by John Janovy, Jr. (on Brightspace)

[http://dl.acm.org/ft_gateway.cfm?id=1378711 ''Voices of Computing''] and [http://dl.acm.org/ft_gateway.cfm?id=1272529 ''Computing is a Natural Science''] by Peter Denning (these links should be accessed from within LMU to get the full article)

|
* What is a model?
** Model of a cell/model of a computer

** [http://brightspace.lmu.edu Slides on Brightspace]

{{Course Schedule Divider}}
|-
! 2
| Tuesday<br/>9/3/2019
|
|
* Discuss the [[Week 1]] assignment
** Wiki syntax overview
** Lab session to setup User wiki page

| rowspan="2" | [[Week 2]]
(due at 12:01am 9/12)

[[Class Journal Week 2]]

|-
!
| Thursday<br/>9/5/2019
|
* [http://www.ncbi.nlm.nih.gov/books/NBK21134/#A5234 Ch. 1 ''Genomes 2'']
* [http://www.ncbi.nlm.nih.gov/books/NBK21121/ Brown, T.A. (2002) ''Genomes 2'', Ch. 3, especially section 3.3.2]
* On [http://brightspace.lmu.edu Brightspace]:
** Nirenberg (2004) ''Deciphering the Genetic Code''
** Kaji & Kaji (2004) ''Setting the Record Straight''
** Moody (2004) ''Digital Code of Life'', Ch. 1
** Hayes (2004) ''Ode to the Code''

|
* DNA structure
** [http://brightspace.lmu.edu Slides on Brightspace]

{{Course Schedule Divider}}
|-
! 3
| Tuesday<br/>9/10/2019
|
|
* Aipotu work session

| rowspan="2" | [[Week 3]]
(due at 12:01am 9/19)

[[Class Journal Week 3]]

|-
!
| Thursday<br/>9/12/2019
|
|
* Genes as models
* Central model of molecular biology
** [http://www.youtube.com/watch?v=u9dhO0iCLww Berkeley translation video]
** This [http://www.dnai.org/a/index.html web site] has links to animations of the replication, transcription, and translation processes.
** [http://brightspace.lmu.edu Slides on Brightspace]
* [[Media:BIOL367_Fall2019_GeneticCode-CentralModel-2.pdf | Paper and pencil exercise]]

{{Course Schedule Divider}}
|-
! 4
| Tuesday<br/>9/17/2019
|
* [http://www.bloomberg.com/graphics/2015-paul-ford-what-is-code/ Ford, Paul. “What is Code?” ''Business Week'', June 11, 2015.]
* [https://www.dataone.org/sites/all/documents/education-modules/pptx/L01_DataManagement.pptx DataONE: Data Management Slides]
|
Introduction to biological databases (part 1)
* What is bioinformatics?
* Open Science Ecosystem
* DataONE: Data Management and the life cycle of data
* Slides are on [http://brightspace.lmu.edu Brightspace]

| rowspan="2" | [[Week 4]]
(due at 12:01am 9/26)

[[Class Journal Week 4]]

|-
!
| Thursday<br/>9/19/2019
|
| Introduction to biological databases (part 2)
* [http://dondi.lmu.build/share/intro/wheres-my-stuff.pdf Where's my Stuff?]
* Slides are on [http://brightspace.lmu.edu Brightspace]
* An overview of computers, networks, files, web applications, web services, and databases
* Names, URLs, identifiers
** As applied to biology: genes, species
* “Stuff” on the worldwide web
** Accessing the web on a browser

{{Course Schedule Divider}}
|-
! 5
| Tuesday<br/>9/24/2019
|
|
[[ Week 4]] work session
| rowspan="2" | [[Week 5]]
(individual due at 12:01am 10/1)

'''''Note Tuesday Due Date'''''

[[Class Journal Week 5]]

(shared due at 12:01am 10/3)

|-
!
| Thursday<br/>9/26/2019
| [https://academic.oup.com/nar/article/47/D1/D1/5280358 The 26th annual Nucleic Acids Research database issue]

[http://www.oxfordjournals.org/nar/database/a/ The NAR Molecular Biology Database Collection]

|
Introduction to biological databases (part 2)
* Terminology, types, evaluation, four "Gold Standard" databases
* A closer look at evaluating biological databases (slides on [http://brightspace.lmu.edu Brightspace])
* [http://www.dataone.org/sites/all/documents/L02_DataSharing.pptx DataONE: Data Sharing]
* [http://brightspace.lmu.edu Presentation Guidelines PowerPoint on Brightspace]

{{Course Schedule Divider}}
|-
! 6
| Tuesday<br/>10/1/2019
|
| Database presentations part 1
# group 1
# group 2
# group 3
# group 4
| rowspan="2" | [[Week 6]]
(due at 12:01am 10/10)

[[Class Journal Week 6]]
|-
!
| Thursday<br/>10/3/2019
|
| Database presentations part 2
# group 1
# group 2
# group 3

[[Presentation Rubric]]

[http://brightspace.lmu.edu/ Presentation Guidelines on Brightspace]

|}

=== Part 2: Going Deeper (Gene Expression Data and Relational Databases) ===

{| class="schedule-table"
! #
! Date
! Reading
! Agenda
! Journal
|-
! 7
| Tuesday<br/>10/8/2019
| For more on the Human Genome Project, see:
* Moody (2004) Chapter 6 (on [http://brightspace.lmu.edu Brightspace])
* [http://www.nature.com/news/2010/100623/pdf/4651000a.pdf Science After the Sequence]
* [http://www.nature.com/news/2010/100623/full/4651000a.html Nature Special: Human Genome at 10]
* [http://www.sciencemag.org/content/vol291/issue5507/index.dtl Human Genome Project articles in ''Science'']
* [http://www.nature.com/nature/journal/v409/n6822/index.html Human Genome Project articles in ''Nature'']
|
* Human Genome Project to DNA microarrays (slides on [http://brightspace.lmu.edu Brightspace])
| rowspan="2" | [[Week 7]]
(due at 12:01am 10/17)

[[Class Journal Week 7]]

|-
!
| Thursday<br/>10/10/2019
| [http://www.ncbi.nlm.nih.gov/books/NBK26818/#A1633 Alberts ''et al''. (2002) ''Molecular Biology of the Cell'', Ch. 8: Microarrays]

[http://www.nature.com/ng/journal/v21/n1s/full/ng0199supp_33.html Brown & Botstein (1999) "Microarrays" ''Nature Genetics'']

Campbell & Heyer Chapter 4 (on [http://brightspace.lmu.edu on Brightspace])

DeRisi et al. (1997) [http://www.sciencemag.org/content/278/5338/680.full ''Science'' 278: 680-686.]
| Review [[Week 6]] assignment

{{Course Schedule Divider}}
|-
! 8
| Tuesday<br/>10/15/2019
| [http://www.dataone.org/sites/all/documents/L04_DataEntryManipulation.pptx DataONE: Data Entry and Manipulation]
| More about DNA microarray analysis
* Slides on [http://brightspace.lmu.edu Brightspace]
| rowspan="2" | [[Week 8]]
(due at 12:01am 10/24)

[[Class Journal Week 8]]
|-
!
| Thursday<br/>10/17/2019
|
| Begin DNA microarray analysis part 1
* Statistical analysis of Dahlquist Lab yeast cold shock dataset
* Test your understanding: [http://xkcd.com/882/ http://xkcd.com/882/]

{{Course Schedule Divider}}
|-
! 9
| Tuesday<br/>10/22/2019
| [https://academic.oup.com/bioinformatics/article/21/suppl_1/i159/203147 Ernst & Bar-Joseph (2006) STEM: a tool for the analysis of short time series gene expression data.]
|
Microarray Data Analysis part 2
* Clustering with stem software



| rowspan="2" | [[Week 9]]
(due at 12:01am 10/31)

[[Class Journal Week 9]]

|-
!
| Thursday<br/>10/24/2019
|
* [https://academic.oup.com/nar/article/46/D1/D348/4191333 Teixeira, et al. (2017). YEASTRACT: an upgraded database for the analysis of transcription regulatory networks in ''Saccharomyces cerevisiae'']
* [https://peerj.com/articles/cs-85/ Dahlquist et al. (2016) GRNsight: a web application and service for visualizing models of small- to medium-scale gene regulatory networks]
|
Microarray Data Analysis part 3
* Finding a candidate GRN ([http://www.yeastract.com/ YEASTRACT])

{{Course Schedule Divider}}
|-
! 10
| Tuesday<br/>10/29/2019
| [https://link.springer.com/article/10.1007/s11538-015-0092-6 Dahlquist et al. (2015) Parameter Estimation for Gene Regulatory Networks from Microarray Data: Cold Shock Response in ''Saccharomyces cerevisiae'']
|
Microarray Data Analysis part 4
* Visualizing the candidate GRN ([https://dondi.github.io/GRNsight/ GRNsight])
* Dynamical systems modeling of the gene regulatory network ([http://kdahlquist.github.io/GRNmap/ GRNmap])

| rowspan="2" | [[Week 10]]
(due at 12:01am 11/7)

[[Class Journal Week 10]]
|-
!
| Thursday<br/>10/31/2019
|
|
Microarray Data Analysis part 5
* Visualizing and interpreting the modeling results ([https://dondi.github.io/GRNsight/ GRNsight])
* Group requests due in class (collected on notecards)
|}

=== Part 3: Integrating for Research (GRNsight Gene Expression Database Project) ===

{| class="schedule-table"
! #
! Date
! Reading
! Agenda
! Journal
|-
! 11
| Tuesday<br/>11/5/2019
|
| Dynamical Systems Modeling with GRNmap
| rowspan="2" | [[Week 11]]

(due at 12:01am 11/14)

'''''Thursday Due Date'''''
|-
!
| Thursday<br/>11/7/2019
|
|
Begin GRNsight Expression Database Project
* Team assignments
* Overview of project
* Work session to create Team wiki pages

{{Course Schedule Divider}}
|-
! 12
| Tuesday<br/>11/12/2019
|
|
Literature searching and annotated bibliographies
* ''Course LibGuide:'' http://libguides.lmu.edu/BIOL367
* Select microarray papers for journal club

| rowspan="2" | [[Week 12/13]]

(due at 12:01am 11/26)

'''''Note Tuesday Due Date'''''

|-
!
| Thursday<br/>11/14/2019
|
| Journal Club Presentations

[[Presentation Rubric]]

[http://brightspace.lmu.edu Presentation Guidelines PowerPoint on Brightspace]

{{Course Schedule Divider}}
|-
! 13
| Tuesday<br/>11/19/2019
|
|
* Status report
* Work session

| rowspan="2" | [[Week 12/13]]

(due at 12:01am 11/26)<br/>
'''''Note Tuesday due date'''''
|-
!
| Thursday<br/>11/21/2019
|
|
* Status report
* Work session

|-
! 14
| Tuesday<br/>11/26/2017
|
|
* Status report
* Work session
| '''''No Week 14 assignment'''''
|-
! style="background: #ded" |
| style="background: #efe; border: none" | Thursday<br/>11/28/2019
| style="background: #efe; border: none" |
| style="background: #efe; border: none" | Thanksgiving—no class
| style="background: #efe; border: none" |
{{Course Schedule Divider}}
|-
! 15
| Tuesday<br/>12/3/2019
|
|
* Status report
* Work session
| rowspan="2" | [[Week 15]]

(due at 4:00pm 12/13)
|-
!
| Thursday<br/>12/5/2019
|
|
* Status report
* Work session
{{Course Schedule Divider}}
|-
! style="background: #edd" | F
| style="background: #fee; border: none" | Tuesday<br/>12/10/2019
| style="background: #fee; border: none" |
| style="background: #fee; border: none" | '''Final project presentations 2:00-4:00 PM'''
| style="background: #fee; border: none" |
|-
!
| Friday<br/>12/13/2019 4:00 PM
|
| '''Project deliverables due 4:00 PM'''
|
|}

== Course Information ==

=== Instructor ===

==== [[User:Kdahlquist | Kam D. Dahlquist, Ph.D.]] ====
: http://kdahlquist.github.io/DahlquistLab/index.htm
: '''Phone:''' (310) 338-7697
: '''E-mail:''' kdahlquist at lmu dot edu
: '''Office:''' Life Sciences Building 289
: '''Office Hours (Fall 2019):''' Mondays & Wednesdays 2:00-3:00 PM, Tuesdays & Thursdays 4:00-5:00 PM, and by appointment; I keep a sign-up sheet next to my office door to facilitate appointment-making.

=== Prerequisites/Recommended Background ===

To take this course, you must have upper division standing in the Seaver College of Science and Engineering. Otherwise, there are no strict prerequisites. Backgrounds in biology and computer science, as well as prior experience with web, database, or information management applications, may be helpful but not necessary.

=== Class Meetings and Attendance ===
TR 2:40–3:55pm, Seaver 120 

This is a hands-on, participatory course, thus attendance at all class meetings is required. Each student is allowed two “sick” days (automatically excused absences) during the semester. Further unexcused absences from class will result in a 5% deduction from the overall course grade for each absence. Every effort should be made to attend class on oral presentation days as the content of that day's class is dependent on student participation. Unexcused absences from an oral presentation will result in a grade of zero for the presentation. The instructor should be notified as soon as possible, electronically or by phone, of the reasons for all absences.

Note that the last day to add or drop a class without a grade of W is August 30. The withdrawal or credit/no-credit status deadline is November 1.

=== Mutual Responsibilities ===

This course is designed to foster your development as a biologist and computer scientist and to give you an authentic research experience. We will be engaged together in discovering, examining, and practicing the personal qualities, technical skills, and community standards of the scientific community. While you are ultimately responsible for your own learning, you are not alone. Our class constitutes a team where we will be learning from each other. The role of the instructor is to provide the expert coaching to support and assist you on your journey. All of the exercises, readings, assignments, and policies detailed below have been designed with this purpose in mind.

=== Classroom Conduct ===

We are all responsible for maintaining a classroom and laboratory environment that is safe and conducive to learning. As such, we will observe the following:

# As an LMU Lion, by the [http://studentaffairs.lmu.edu/wellness/lmucares/aboutlmucares/livethelionscode/ Lion’s code], you are pledged to join the discourse of the academy with honesty of voice and integrity of scholarship and to show respect for staff, professors, and other students.
# You are responsible for your own learning and for being a good class citizen.
# Class will start promptly on time.
# You are expected to come to class having done the assigned reading and preparatory work so that you are ready to participate in discussions and to perform the laboratory exercises.
# You are expected to bring the required materials to each class session.
# Cell phones and other ancillary devices must be turned off and put away out of sight. Your own laptop and/or tablet may be used to conduct the class exercises, '''''provided that you have confirmed with the instructor that you have the correct versions of software installed for the exercise'''''. If, however, the laptops/tablets are being used for other purposes and become distracting to you or others, you will be asked to put them away.
# All students are governed by [https://studentaffairs.lmu.edu/media/studentaffairs/osccr/documents/1920-LMU-community-standards.pdf LMU Community Standards publication]. Disruptive behavior which is persistent or significantly interferes with classroom activities may be subject to disciplinary action. A student may be referred to the Office of Student Judicial Affairs if his or her behavior constitutes a violation of the conduct code.

=== Course Website ===

This is the course web site and wiki, hosted by http://xmlpipedb.cs.lmu.edu/biodb/fall2019/. You will need to have a user account on this wiki to be able to edit the wiki and complete coursework. Updates to the course schedule and electronic copies of all handouts, assignments, and readings will be posted to this site. You will also use the site to keep an electronic lab notebook/journal for the course. In addition, students have been automatically enrolled in the course page on [http://brightspace.lmu.edu Brightspace]. The site will be used for materials that cannot be made public on this wiki, including grades.

=== Email Communication ===

At times we will communicate with the entire class using campus email systems, so it is essential that you regularly check your lion.lmu.edu email address or forward your lion account email to your preferred email address. Messages sent to the instructor at night or on the weekend will be answered the next school day.

=== Required Materials ===

==== Texts ====

There is no required text to purchase for the course; materials will be put on reserve at Hannon Library or will be available online on this wiki or [http://brightspace.lmu.edu Brightspace] site. Specific reading assignments are given on the course schedule and should be completed before coming to class.
* Assorted handouts, articles, and sample code will be distributed throughout the semester.
* Additional information is also available on the web; do not hesitate to look for further sources of information regarding the concepts, techniques, tools, and paradigms that we will discuss.

==== Materials (must be brought to each class meeting) ====

* 3-ring binder with all course handouts
* Pen, pencil, extra paper
* USB flash drive to store data
* Account for this wiki
* [http://www.github.com GitHub account]

=== Course Description ===

The disciplines of biology and computer science come together in bioinformatics, where computational tools are needed to manage and analyze the flood of data coming from new genomics technologies. Biological databases form a significant part of this young and exciting field. This course introduces students to both the biology and computer science expertise needed to understand, use, and develop biological databases. Biology topics include the fundamentals of genetics, molecular biology, and biochemistry needed to understand the data stored in biological databases, as well as the biotechnologies used to gather these data in a high-throughput manner. Computer science topics include what biological databases are, why they are important (and needed), and the challenges that arise in compiling them effectively. Biology and computer science lectures on topics that are relevant to biological databases are coupled with hands-on experience with a variety of software packages ranging from bioinformatics utilities to general-purpose database and software development tools. After learning how to use a biological database, students will be asked to build one of their own.

==== Course Objectives and Learning Outcomes ====

This course is built upon L. Dee Fink’s ''taxonomy of significant learning'', as applied to biological databases. Long after the course concludes, our hope is that:
* You understand how biological information is encoded in the genome and can apply this knowledge to a variety of biological tasks and problems
* You understand the core concepts, structure, and functions of a database, ranging from individual files to a full relational database management system, and can perform useful tasks with such data
* You show discipline and proficiency in day-to-day science and engineering best practices, such as maintaining journals and notebooks, managing your files and code, and critically evaluating scientific and technical information
* You recognize and care about how the biological and technological issues presented in this course relate to and affect society, our daily lives, and ourselves
* You have some skills and tools for “leaving your comfort zone,” flourishing outside of it, and learning more about biology and computer science on your own
* You learn how to communicate and work effectively with colleagues from different disciplines

==== University Core Curriculum ====

This course fulfills the following requirements in the University Core Curriculum:
* Integrations: Interdisciplinary Connections
* Upper Division Information Literacy Flag
* Upper Division Oral Communication Flag

=== Course Work and Grading ===

Your work in this course will be assessed in the following areas:

{| style="border: 1px dashed rgb(47, 111, 171); background: rgb(249, 249, 249); margin: 1em 0; padding: 1em" cellpadding="5ex"
| Weekly electronic lab notebook/journal assignments, individual (usually 10 points each)
| style="text-align: right" | 150
| points
|-
| Weekly electronic lab notebook/journal assignments, shared (usually 3 points each)
| style="text-align: right" | 50
| points
|-
| Oral presentations
| style="text-align: right" | 120
| points 
|-
| Final Project Deliverables (including written report)
| style="text-align: right" | 100
| points
|-
| '''Total'''
| style="text-align: right" | '''420'''
| '''points'''
|}

Final course grading scale:
94.0-100.0% A
90.0- 93.9% A-
86.0- 89.9% B+
82.0- 85.9% B
78.0- 81.9% B-
74.0- 77.9% C+
70.0- 73.9% C
67.0- 69.9% C-
60.0- 66.9% D
≤ 59.9% F

Current grades will be made available at [http://brightspace.lmu.edu Brightspace].

==== Electronic Laboratory Notebook/Journal ====

One of the most important skills you can develop as a scientist is keeping an excellent laboratory notebook. For computational research, the equivalent of the biology paper-based lab notebook is documentation of your “workflow.” For this course you will practice documentation skills by keeping an electronic lab notebook or journal. The technology we will use is this wiki. We will create and edit during the semester. The wiki software is the same one that runs Wikipedia and many other wiki sites, so proficiency gained here is easily transferable to other applications.

You will create an individual user page and make weekly entries that the instructor will read and grade. You will use the wiki to complete the assignments as well. The following guidelines apply:
* Your weekly journal entry is typically due every 12:01am on Thursday Pacific time; consult the schedule for specific due dates for each assignment.
* Each weekly assignment has an individual component and a shared component. You will earn 10 points per weekly submission for the individual journal entry and 3 points per submission for the shared journal entry. '''''Late journal entries will be accepted up to one week later for up to half credit.'''''
* The instructor will read and comment on how to improve your journal entries on your user talk pages.
* Depending on the type of assignment for that week, you may be given the opportunity to make improvements to previous journal entries as the semester progresses.
* For most weeks in the semester, you will be assigned a "homework partner". You will be expected to consult with your partner in order to complete the assignment. However, unless otherwise stated, each partner must submit his or her own work as the individual journal entry (direct copies of each other's work is not allowed).
* Generally, your journal entries will consist of:
** Your electronic laboratory notebook (workflow and other documentation) for hands-on exercises and projects
** Answers to any specific questions posed in the exercise
** Acknowledgments section (see [[Week 1]] assignment for details)
** References section (see [[Week 1]] assignment for details)
** Shared reflection on your learning, assigned readings, or ethics case studies.

==== Oral Presentations ====

You will give three oral presentations in this course. The first two will be in the format of a “Journal Club” presentation where students will present and lead discussion of research articles from the primary literature. The third will be a research presentation on your final project. '''''Because that day’s class content is dependent upon each student being ready to present and lead discussion, late journal club presentations will not be accepted. An unexcused absence from a journal club presentation will result in a grade of zero for the presentation.'''''

==== Final Group Project ====



In addition to the research presentation, the culmination of your final project will be the preparation of a written laboratory report in the style of a manuscript that could be submitted to a peer-reviewed journal. Specific instructions will be posted later in the semester. '''''The Final Written Report cannot be accepted any later than Friday, December 13 at 4:00pm. The Final Written Report must be completed to receive a passing grade in the course.'''''

==== Extra Credit ====

Students may accumulate up to 12.5 points toward their final grade in extra credit by attending Biology Department seminars and completing the seminar sheets. Each seminar attended is worth 2.5 points with up to 5 seminars (12.5 points) total. You must attend the entire seminar from start to finish and personally turn in your seminar sheet to a faculty member at the end of the seminar.

Certain non-Biology Department seminars may be approved in advance for extra credit at the instructor's discretion. To receive credit for these seminars, you must turn in a one-page '''''hard copy''''' of your summary of the seminar '''''in class, within one week''''' of the date of the seminar or they will not count as extra credit.

=== Work Load Expectations ===

In line with [http://www.lmu.edu/Assets/LMU+Credit+Hour+Policy_Final.pdf LMU’s Credit Hour Policy], the work load expectation for this course is that for every one hour (50 minutes) of classroom instruction, you will complete a minimum of two hours of out-of-class student work each week. This is a 3-unit course with 3 hours (150 minutes) of instruction per week. Thus the expectation is that you will complete 6 hours of work outside of class per week.

=== University Policy on Academic Honesty ===

Loyola Marymount University is a community dedicated to academic excellence. Academic honesty in scholarship and creative work stands at the center of LMU's academic life, and is essential for true learning and creation of knowledge to take place. As a university in the Jesuit and Marymount traditions, this community expects its members to act in accordance with the highest standards of honesty and ethics at all times. Violations of academic honesty undermine the fundamental educational mission of the University and cannot be tolerated.

Academic dishonesty will be treated as an extremely serious matter with severe consequences. '''''The minimum penalty for an instance of academic dishonesty in this course, even on a 1-point assignment or extra credit assignment, is a one-letter grade penalty in the course and a zero on the assignment.''''' It is never permissible to turn in any work that has not been authored by the student, such as work that has been copied from another student or copied from a source (including Internet) without properly acknowledging the source. It is the student's responsibility to make sure that your work meets the standard set forth in the “Academic Honesty Policy” (see http://academics.lmu.edu/honesty.) You are responsible for contacting the instructor before assignments are due to proactively resolve any questions you may have.

You are required to sign the [[Media:BIOL367_Fall2019_AcademicHonestyAgreement.pdf|Academic Honesty Agreement]] for this course.

==== Academic Honesty Resources ====
* [[Media:BIOL367_Fall2019_AcademicHonestyAgreement.pdf|Academic Honesty Agreement]]
* [http://academics.lmu.edu/media/lmuacademics/academichonesty/documents/Academic%20Honesty%20Policy%20FINAL%20Appendices--051116.pdf LMU Academic Honesty Policy and Procedures]
* [http://www.bio.davidson.edu/dept/plagiarism.html The Davidson College Department of Biology Statement on Plagiarism]
* [[Media:BIOL367_Fall2019_GuidelinesforLiteratureCitations.pdf | Guidelines for Literature Citations in a Scientific Paper]]
** [https://owl.purdue.edu/owl/research_and_citation/apa_style/apa_formatting_and_style_guide/reference_list_basic_rules.html APA Style for References]
* [http://dondi.lmu.build/share/misc/plagiarism.pdf Notes on plagiarism from an electrical engineering & computer science perspective]
* [http://libguides.lmu.edu/BIOL367 BIOL/CMSI 367 LibGuide]

=== Americans with Disabilities Act—Special Accommodations ===

Students with special needs who require reasonable modifications, special assistance, or accommodations in this course should promptly direct their request to the Disability Support Services (DSS) Office. Any student who currently has a documented disability (ADHD, Autism Spectrum Disorder, Learning, Physical, or Psychiatric) needing academic accommodations should contact the DSS Office (Daum Hall 2nd floor, 310-338-4216) as early in the semester as possible. All discussions will remain confidential. Please visit http://www.lmu.edu/dss for additional information. In addition, please schedule an appointment with the instructor early in the semester to discuss any accommodations for this course for which you have been approved.

=== Health and Wellness ===

There are many resources for student wellness on campus. A central website for students is: http://lionwellness.lmu.edu.

=== Emergency Preparedness ===

To report an emergency or suspicious activity, contact the LMU Department of Public Safety by phone (x222 or 310-338-2894) or at the nearest emergency call box. In the event of an evacuation, follow the evacuation signage throughout the building to the designated safe refuge area where you will receive further instruction from Public Safety or a Building Captain. For more safety information and preparedness tips, visit http://www.lmu.edu/emergency.

=== Revision Notice ===

If necessary, this syllabus and its contents are subject to revision; students are responsible for any changes or modifications announced in class. The most current version of this information resides on this page, the course web site at http://xmlpipedb.cs.lmu.edu/biodb/fall2019/.

Coder/Designer

2019-11-26T23:32:41Z

Kdahlquist: /* Milestone 4: Design a Database to Store Time-course Microarray Data from four sources */ add GEO id

{{Final Project Links}}

The Coder/Designer is responsible for creating the Microsoft Access database that will be used by the Data Analyst to prepare an input workbook for GRNmap for the microarray dataset they are analyzing. The Coder/Designer is also the resident expert on the technology being used—assorted software, file management, version control, and troubleshooting. He or she coordinates with Dr. Dahlquist and fellow Coders/Designers in developing the Access database and storing it on Box.

== Guild Members ==

* [[Sulfiknights]]: DeLisa Madere
* [[FunGals]]: Mike Armas
* [[Skinny Genes]]: Mihir

== Milestones ==

The milestones do not necessarily correspond to particular weeks; instead they are sets of tasks grouped together. However, Milestone 3 is a hard prerequisite for proceeding to Milestone 4, so ideally the Coder/Coder guild should finish these milestones (they require some coordination; see below) as soon as possible.

=== Milestone 1: Annotated Bibliography ===

* The Coder/Designer will work with their teams to develop an annotated bibliography of papers relating to their team's assigned paper.

=== Milestone 2: Journal Club Presentation ===

* The Coder/Designer will work with their teams to create and deliver a Journal Club presentation about to their team's assigned paper.

=== Milestone 3: Working Environment Setup ===

Coder/Designer work will require the following software/accounts. The Seaver 120 lab computers are already set up for this; this list is provided for Coders/Designers who need to work on a different computer or outside of the lab.
* Microsoft Access
* Box account (provided by LMU)
** Databases created by the teams will be kept in a [https://lmu.box.com/s/gutpb5qm0a6b2pvjn1j6moqb6y47e903 "BIOL367_Fall2019 > Final Project Database" Box folder].
** Coder/Designer guild members have rights as editor to this folder; all others in the class can only view/download.
** This folder will serve as as the version control mechanism for the Coder/Designer guild.

=== Milestone 4: Design a Database to Store Time-course Microarray Data from four sources ===

* Designer/Coders will work with the QA's to create a MS Access Database to store the yeast time-course microarray data for the dataset being analyzed by the Data Analysts.
* The starting point will be the database already used for the [[Week 10]] assignment, which can be found [https://lmu.box.com/s/kn8l6r639af683ioojqoce5w1g3z7kd2 here] on Box.
** This database is already populated with tables for the Dahlquist Lab microarray data, degradation rates from Neymotin et al. (2014), and initial guesses for production rates.
*** You may need to change the table names of these existing tables so that they make sense with the overall database design.
** You will need to add one or more expression tables for the expression data from your team's article.
*** Work with your team's QA and Data Analysts to determine appropriate column headings for the expression table.
** You will also need to create one or more tables with metadata about the other tables because now the database will contain data from multiple sources, not just one.
*** A major part of the design work will be to figure out what information needs to be in the metadata table so that queries can be easily and uniquely performed on the data.
*** Think about what information would someone need to know to be able to understand how the dataset works. Consult with the QA and Data Analysts to figure out the sample-data relationships and how that should be encoded.
* The GEO accession number for the Dahlquist lab data is GSE83656.

=== Milestone 5: Build an individual database for your team ===

* Once the design work has been completed, you need to actually import the data into the database.
* Initially, each team will have their own database so that the QA and Data analysts can validate and use the database.

=== Milestone 6: Validation and Quality Assurance on Database ===

* The QA will perform quality assurance to make sure that the database is correct and accurate.
** In particular, the QA needs to make sure that all of the rows of data were imported into the database for the expression table(s).
** The QA will make sure that both the ID (SGD systematic name) and Standard Names are included in each expression table and are correct.
* QA's will communicate to the Coder/Designers any changes needed to the database.

=== Milestone 7: Merge completed databases into a single database for the class ===

* As a guild, the Coder/Designers will merge their separate databases into a final product.
* With the QA's finalize the [https://www.quackit.com/microsoft_access/microsoft_access_2016/howto/how_to_create_a_database_diagram_in_access_2016.cfm database schema diagram]

{{Final Project Links}}

[[Category:Group Projects]]

Data Analysis

2019-11-26T23:28:40Z

Kdahlquist: /* Milestone 5: Clustering with stem and YEASTRACT */ instructions on how to use GO for enrichment

{{Final Project Links}}

The role of the Data Analyst will be to apply the data analysis pipeline that you learned by analyzing the Dahlquist Lab microarray dataset to complete the analysis of a different published yeast timecourse microarray dataset. The Data Analysts are the end-users of the project, ultimately determining whether the work of the coder/designer and quality assurance members is useful to them.

== Guild Members ==

* [[Sulfiknights]]: Ivy, Marcus
* [[FunGals]]: Emma, Kaitlyn
* [[Skinny Genes]]: Aby, David

== Milestones ==

The milestones do not necessarily correspond to particular days/weeks; instead they are sets of tasks grouped together.

* Data Analysts and QA's who have a partner in their group can have a shared ''individual'' journal entry. Both students will be given the same grade and are expected to contribute equally to the electronic lab notebook.

=== Milestone 1: Annotated Bibliography ===

* The Data Analysts will work with their teams to develop an annotated bibliography of papers relating to their team's assigned paper.

=== Milestone 2: Journal Club Presentation ===

* The Data Analysts will work with their teams to create and deliver a Journal Club presentation about to their team's assigned paper.

=== Milestone 3: Getting the data ready for analysis ===

# Download and examine the microarray dataset, comparing it to the samples and experiment described in your journal club article.
#* [[Skinny Genes]]: [https://sgd-prod-upload.s3.amazonaws.com/S000204227/Barreto_2012_PMID_23039231.zip Barreto et al. (2012)]
#* [[FunGals]]: [https://sgd-prod-upload.s3.amazonaws.com/S000204415/Kitagawa_2002_PMID_12269742.zip Kitagawa et al. (2002)]
#* [[Sulfiknights]]: [https://sgd-prod-upload.s3.amazonaws.com/S000204367/Thorsen_2007_PMID_17327492.zip Thorsen et al. (2007)]
# Along with the QA's, make a "sample-data relationship table" that lists all of the samples (microarray chips), noting the treatment, time point, and replicate number.
#* Come up with consistent column headers that summarize this information
#** For example, the Dahlquist Lab microarray data used strain_LogFC_timepoint-replicate number, as in wt_LogFC_t15-1.
# Organize the data in a worksheet in an Excel workbook so that:
#* ID is in the first column
#* Data columns are to the right, in increasing chronological order, using the column header pattern you created
#* Replicates are grouped together

=== Milestone 4: ANOVA analysis ===

# Perform an ANOVA analysis of the data, as you did on [[Week 8]] for the Dahlquist lab data.
#* Note that you will need to adjust your formulas to take into account the different number of timepoints and replicates in your article's dataset.

=== Milestone 5: Clustering with stem and YEASTRACT ===

# Cluster the data with stem, as you did on [[Week 9]].
#* Note that we will make some adjustments to the GO term analysis because stem was not providing GO term names. We are going to use the GO enrichment tool at GeneOntology.org instead.
## Go to [http://geneontology.org/ http://geneontology.org/].
## For the cluster you want to analyze, open the gene list and copy the list of genes.
## Paste the list of genes into the "Go Enrichment Analysis" box on the right hand side of the GeneOntology.org page.
## Select "Saccharomyces cerevisiae" from the species drop-down menu.
## Click the "Launch" buton.
## Near the bottom of the results page, click on the button to Export "Table".
## This will prompt you to save a .txt file that can be opened in Excel to view your results.
# Use YEASTRACT to generate a candidate gene regulatory network as you did on [[Week 9]].

=== Milestone 6: Create an input workbook for GRNmap using MS Access database ===

# Create an input workbook for GRNmap based on a Microsoft Access database that the Coder/Designer and QA's make, following protocol in [[Week 10]]
# Run GRNmap and interpret data.
# As the end-user of the Access database, the Data Analysts will provide feedback to the QAs and Coder/Designer about the usability of database.

{{Final Project Links}}

[[Category:Group Projects]]

Main Page

2019-11-26T20:47:37Z

Kdahlquist: /* Upcoming Seminars */ delete old, add new

__NOTOC__
<div style="text-align: center; font-size: 175%">Loyola Marymount University</div>
<div style="text-align: center; font-size: 250%; line-height: 1.25em">'''BIOL 367-01: Biological Databases'''</div>
<div style="text-align: center; font-size: 175%; margin-bottom: 0.85em">Fall 2019</div>

<div class="toc-container">__TOC__</div>

== Announcements ==

* Data Analysts and QA's who have a partner in their group can have a shared ''individual'' journal entry. Both students will be given the same grade and are expected to contribute equally to the electronic lab notebook.

=== Upcoming Seminars ===

* '''LMU Society of Physics Students:''' Wednesday, December 4, 4:30 - 6:00 pm, Von der Ahe 190, Dr. Brian Keating, author of ''Losing the Nobel Prize'' will engage students with his research on imaging the cosmic background radiation, a remnant of the Big Bang.

== Instructor ==
* [[User:Kdahlquist|Kam D. Dahlquist]]

== Students ==

{| style="margin-left: auto; margin-right: auto"
| style="padding-right: 1em" | [[User:Marmas|Michael Armas]]
| style="padding-right: 1em" | [[User:Cdomin12|Christina Dominguez]]
| style="padding-right: 1em" | [[User:knguye66|Kaitlyn Nguyen]]
| style="padding-right: 1em" | [[User:Ntesfaio|Naomi Tesfaiohannes]]
|-
| style="padding-right: 1em" | [[User:mavila9|Marcus Avila]]
| style="padding-right: 1em" | [[User:Imacarae|Ivy-Quynh Macaraeg]]
| style="padding-right: 1em" | [[User:Jnimmers| John Nimmers-Minor]]
| style="padding-right: 1em" | [[User:eyoung20|Emma Young]]
|-
| style="padding-right: 1em" | [[User:Jcowan4|Jonar Cowan]]
| style="padding-right: 1em" | [[User:Dmadere | DeLisa Madere]]
| style="padding-right: 1em" | [[User:dramir36| David Ramirez]]
| style="padding-right: 1em" |
|-
| style="padding-right: 1em" | [[User:Icrespin| Iliana Crespin]]
| style="padding-right: 1em" | [[User:ymesfin| Yeabsira Mesfin]]
| style="padding-right: 1em" | [[User:msamdars| Mihir Samdarshi]]
| style="padding-right: 1em" |
|}

{{Final Project Links}}

== Course Schedule ==

The current course schedule is shown below. In addition to the listed in-class agendas, the following guidelines are also notable:

* Unless otherwise stated on the schedule, your weekly journals/assignments are due on Thursdays at 12:01am. In cases where subsequent revisions or corrections will be accepted, the instructor will provide feedback and submission deadlines on a per-assignment basis.
* Reading assignments should be completed ''before'' coming to class.

=== Part 1: Building Blocks (Genetic Code and Manipulating Text) ===

{| class="schedule-table"
! #
! Date
! Reading
! Agenda
! Journal
|-
! 1
| Tuesday<br/>8/27/2019
|
|
* Syllabus walkthrough
* Pairwise interviews and introductions
* Academic Honesty case studies
* Class wiki account: Set up a username and password for this wiki site
| rowspan="2" | [[Week 1]]
(due at 12:01am 9/5)

[[Class_Journal_Week_1 | Class Journal Week 1]]
|-
!
| Thursday<br/>8/29/2019
| [http://brightspace.lmu.edu Chapter 1 of ''On Becoming a Biologist''] by John Janovy, Jr. (on Brightspace)

[http://dl.acm.org/ft_gateway.cfm?id=1378711 ''Voices of Computing''] and [http://dl.acm.org/ft_gateway.cfm?id=1272529 ''Computing is a Natural Science''] by Peter Denning (these links should be accessed from within LMU to get the full article)

|
* What is a model?
** Model of a cell/model of a computer

** [http://brightspace.lmu.edu Slides on Brightspace]

{{Course Schedule Divider}}
|-
! 2
| Tuesday<br/>9/3/2019
|
|
* Discuss the [[Week 1]] assignment
** Wiki syntax overview
** Lab session to setup User wiki page

| rowspan="2" | [[Week 2]]
(due at 12:01am 9/12)

[[Class Journal Week 2]]

|-
!
| Thursday<br/>9/5/2019
|
* [http://www.ncbi.nlm.nih.gov/books/NBK21134/#A5234 Ch. 1 ''Genomes 2'']
* [http://www.ncbi.nlm.nih.gov/books/NBK21121/ Brown, T.A. (2002) ''Genomes 2'', Ch. 3, especially section 3.3.2]
* On [http://brightspace.lmu.edu Brightspace]:
** Nirenberg (2004) ''Deciphering the Genetic Code''
** Kaji & Kaji (2004) ''Setting the Record Straight''
** Moody (2004) ''Digital Code of Life'', Ch. 1
** Hayes (2004) ''Ode to the Code''

|
* DNA structure
** [http://brightspace.lmu.edu Slides on Brightspace]

{{Course Schedule Divider}}
|-
! 3
| Tuesday<br/>9/10/2019
|
|
* Aipotu work session

| rowspan="2" | [[Week 3]]
(due at 12:01am 9/19)

[[Class Journal Week 3]]

|-
!
| Thursday<br/>9/12/2019
|
|
* Genes as models
* Central model of molecular biology
** [http://www.youtube.com/watch?v=u9dhO0iCLww Berkeley translation video]
** This [http://www.dnai.org/a/index.html web site] has links to animations of the replication, transcription, and translation processes.
** [http://brightspace.lmu.edu Slides on Brightspace]
* [[Media:BIOL367_Fall2019_GeneticCode-CentralModel-2.pdf | Paper and pencil exercise]]

{{Course Schedule Divider}}
|-
! 4
| Tuesday<br/>9/17/2019
|
* [http://www.bloomberg.com/graphics/2015-paul-ford-what-is-code/ Ford, Paul. “What is Code?” ''Business Week'', June 11, 2015.]
* [https://www.dataone.org/sites/all/documents/education-modules/pptx/L01_DataManagement.pptx DataONE: Data Management Slides]
|
Introduction to biological databases (part 1)
* What is bioinformatics?
* Open Science Ecosystem
* DataONE: Data Management and the life cycle of data
* Slides are on [http://brightspace.lmu.edu Brightspace]

| rowspan="2" | [[Week 4]]
(due at 12:01am 9/26)

[[Class Journal Week 4]]

|-
!
| Thursday<br/>9/19/2019
|
| Introduction to biological databases (part 2)
* [http://dondi.lmu.build/share/intro/wheres-my-stuff.pdf Where's my Stuff?]
* Slides are on [http://brightspace.lmu.edu Brightspace]
* An overview of computers, networks, files, web applications, web services, and databases
* Names, URLs, identifiers
** As applied to biology: genes, species
* “Stuff” on the worldwide web
** Accessing the web on a browser

{{Course Schedule Divider}}
|-
! 5
| Tuesday<br/>9/24/2019
|
|
[[ Week 4]] work session
| rowspan="2" | [[Week 5]]
(individual due at 12:01am 10/1)

'''''Note Tuesday Due Date'''''

[[Class Journal Week 5]]

(shared due at 12:01am 10/3)

|-
!
| Thursday<br/>9/26/2019
| [https://academic.oup.com/nar/article/47/D1/D1/5280358 The 26th annual Nucleic Acids Research database issue]

[http://www.oxfordjournals.org/nar/database/a/ The NAR Molecular Biology Database Collection]

|
Introduction to biological databases (part 2)
* Terminology, types, evaluation, four "Gold Standard" databases
* A closer look at evaluating biological databases (slides on [http://brightspace.lmu.edu Brightspace])
* [http://www.dataone.org/sites/all/documents/L02_DataSharing.pptx DataONE: Data Sharing]
* [http://brightspace.lmu.edu Presentation Guidelines PowerPoint on Brightspace]

{{Course Schedule Divider}}
|-
! 6
| Tuesday<br/>10/1/2019
|
| Database presentations part 1
# group 1
# group 2
# group 3
# group 4
| rowspan="2" | [[Week 6]]
(due at 12:01am 10/10)

[[Class Journal Week 6]]
|-
!
| Thursday<br/>10/3/2019
|
| Database presentations part 2
# group 1
# group 2
# group 3

[[Presentation Rubric]]

[http://brightspace.lmu.edu/ Presentation Guidelines on Brightspace]

|}

=== Part 2: Going Deeper (Gene Expression Data and Relational Databases) ===

{| class="schedule-table"
! #
! Date
! Reading
! Agenda
! Journal
|-
! 7
| Tuesday<br/>10/8/2019
| For more on the Human Genome Project, see:
* Moody (2004) Chapter 6 (on [http://brightspace.lmu.edu Brightspace])
* [http://www.nature.com/news/2010/100623/pdf/4651000a.pdf Science After the Sequence]
* [http://www.nature.com/news/2010/100623/full/4651000a.html Nature Special: Human Genome at 10]
* [http://www.sciencemag.org/content/vol291/issue5507/index.dtl Human Genome Project articles in ''Science'']
* [http://www.nature.com/nature/journal/v409/n6822/index.html Human Genome Project articles in ''Nature'']
|
* Human Genome Project to DNA microarrays (slides on [http://brightspace.lmu.edu Brightspace])
| rowspan="2" | [[Week 7]]
(due at 12:01am 10/17)

[[Class Journal Week 7]]

|-
!
| Thursday<br/>10/10/2019
| [http://www.ncbi.nlm.nih.gov/books/NBK26818/#A1633 Alberts ''et al''. (2002) ''Molecular Biology of the Cell'', Ch. 8: Microarrays]

[http://www.nature.com/ng/journal/v21/n1s/full/ng0199supp_33.html Brown & Botstein (1999) "Microarrays" ''Nature Genetics'']

Campbell & Heyer Chapter 4 (on [http://brightspace.lmu.edu on Brightspace])

DeRisi et al. (1997) [http://www.sciencemag.org/content/278/5338/680.full ''Science'' 278: 680-686.]
| Review [[Week 6]] assignment

{{Course Schedule Divider}}
|-
! 8
| Tuesday<br/>10/15/2019
| [http://www.dataone.org/sites/all/documents/L04_DataEntryManipulation.pptx DataONE: Data Entry and Manipulation]
| More about DNA microarray analysis
* Slides on [http://brightspace.lmu.edu Brightspace]
| rowspan="2" | [[Week 8]]
(due at 12:01am 10/24)

[[Class Journal Week 8]]
|-
!
| Thursday<br/>10/17/2019
|
| Begin DNA microarray analysis part 1
* Statistical analysis of Dahlquist Lab yeast cold shock dataset
* Test your understanding: [http://xkcd.com/882/ http://xkcd.com/882/]

{{Course Schedule Divider}}
|-
! 9
| Tuesday<br/>10/22/2019
| [https://academic.oup.com/bioinformatics/article/21/suppl_1/i159/203147 Ernst & Bar-Joseph (2006) STEM: a tool for the analysis of short time series gene expression data.]
|
Microarray Data Analysis part 2
* Clustering with stem software



| rowspan="2" | [[Week 9]]
(due at 12:01am 10/31)

[[Class Journal Week 9]]

|-
!
| Thursday<br/>10/24/2019
|
* [https://academic.oup.com/nar/article/46/D1/D348/4191333 Teixeira, et al. (2017). YEASTRACT: an upgraded database for the analysis of transcription regulatory networks in ''Saccharomyces cerevisiae'']
* [https://peerj.com/articles/cs-85/ Dahlquist et al. (2016) GRNsight: a web application and service for visualizing models of small- to medium-scale gene regulatory networks]
|
Microarray Data Analysis part 3
* Finding a candidate GRN ([http://www.yeastract.com/ YEASTRACT])

{{Course Schedule Divider}}
|-
! 10
| Tuesday<br/>10/29/2019
| [https://link.springer.com/article/10.1007/s11538-015-0092-6 Dahlquist et al. (2015) Parameter Estimation for Gene Regulatory Networks from Microarray Data: Cold Shock Response in ''Saccharomyces cerevisiae'']
|
Microarray Data Analysis part 4
* Visualizing the candidate GRN ([https://dondi.github.io/GRNsight/ GRNsight])
* Dynamical systems modeling of the gene regulatory network ([http://kdahlquist.github.io/GRNmap/ GRNmap])

| rowspan="2" | [[Week 10]]
(due at 12:01am 11/7)

[[Class Journal Week 10]]
|-
!
| Thursday<br/>10/31/2019
|
|
Microarray Data Analysis part 5
* Visualizing and interpreting the modeling results ([https://dondi.github.io/GRNsight/ GRNsight])
* Group requests due in class (collected on notecards)
|}

=== Part 3: Integrating for Research (GRNsight Gene Expression Database Project) ===

{| class="schedule-table"
! #
! Date
! Reading
! Agenda
! Journal
|-
! 11
| Tuesday<br/>11/5/2019
|
| Dynamical Systems Modeling with GRNmap
| rowspan="2" | [[Week 11]]

(due at 12:01am 11/14)

'''''Thursday Due Date'''''
|-
!
| Thursday<br/>11/7/2019
|
|
Begin GRNsight Expression Database Project
* Team assignments
* Overview of project
* Work session to create Team wiki pages

{{Course Schedule Divider}}
|-
! 12
| Tuesday<br/>11/12/2019
|
|
Literature searching and annotated bibliographies
* ''Course LibGuide:'' http://libguides.lmu.edu/BIOL367
* Select microarray papers for journal club

| rowspan="2" | [[Week 12/13]]

(due at 12:01am 11/26)

'''''Note Tuesday Due Date'''''

|-
!
| Thursday<br/>11/14/2019
|
| Journal Club Presentations

[[Presentation Rubric]]

[http://brightspace.lmu.edu Presentation Guidelines PowerPoint on Brightspace]

{{Course Schedule Divider}}
|-
! 13
| Tuesday<br/>11/19/2019
|
|
* Status report
* Work session

| rowspan="2" | [[Week 12/13]]

(due at 12:01am 11/26)<br/>
'''''Note Tuesday due date'''''
|-
!
| Thursday<br/>11/21/2019
|
|
* Status report
* Work session

|-
! 14
| Tuesday<br/>11/26/2017
|
|
* Status report
* Work session
| '''''No Week 14 assignment'''''
|-
! style="background: #ded" |
| style="background: #efe; border: none" | Thursday<br/>11/28/2019
| style="background: #efe; border: none" |
| style="background: #efe; border: none" | Thanksgiving—no class
| style="background: #efe; border: none" |
{{Course Schedule Divider}}
|-
! 15
| Tuesday<br/>12/3/2019
|
|
* Status report
* Work session
| rowspan="2" | [[Week 15]]

(due at 4:00pm 12/13)
|-
!
| Thursday<br/>12/5/2019
|
|
* Status report
* Work session
{{Course Schedule Divider}}
|-
! style="background: #edd" | F
| style="background: #fee; border: none" | Tuesday<br/>12/10/2019
| style="background: #fee; border: none" |
| style="background: #fee; border: none" | '''Final project presentations 2:00-4:00 PM'''
| style="background: #fee; border: none" |
|-
!
| Friday<br/>12/13/2019 4:00 PM
|
| '''Project deliverables due 4:00 PM'''
|
|}

== Course Information ==

=== Instructor ===

==== [[User:Kdahlquist | Kam D. Dahlquist, Ph.D.]] ====
: http://kdahlquist.github.io/DahlquistLab/index.htm
: '''Phone:''' (310) 338-7697
: '''E-mail:''' kdahlquist at lmu dot edu
: '''Office:''' Life Sciences Building 289
: '''Office Hours (Fall 2019):''' Mondays & Wednesdays 2:00-3:00 PM, Tuesdays & Thursdays 4:00-5:00 PM, and by appointment; I keep a sign-up sheet next to my office door to facilitate appointment-making.

=== Prerequisites/Recommended Background ===

To take this course, you must have upper division standing in the Seaver College of Science and Engineering. Otherwise, there are no strict prerequisites. Backgrounds in biology and computer science, as well as prior experience with web, database, or information management applications, may be helpful but not necessary.

=== Class Meetings and Attendance ===
TR 2:40–3:55pm, Seaver 120 

This is a hands-on, participatory course, thus attendance at all class meetings is required. Each student is allowed two “sick” days (automatically excused absences) during the semester. Further unexcused absences from class will result in a 5% deduction from the overall course grade for each absence. Every effort should be made to attend class on oral presentation days as the content of that day's class is dependent on student participation. Unexcused absences from an oral presentation will result in a grade of zero for the presentation. The instructor should be notified as soon as possible, electronically or by phone, of the reasons for all absences.

Note that the last day to add or drop a class without a grade of W is August 30. The withdrawal or credit/no-credit status deadline is November 1.

=== Mutual Responsibilities ===

This course is designed to foster your development as a biologist and computer scientist and to give you an authentic research experience. We will be engaged together in discovering, examining, and practicing the personal qualities, technical skills, and community standards of the scientific community. While you are ultimately responsible for your own learning, you are not alone. Our class constitutes a team where we will be learning from each other. The role of the instructor is to provide the expert coaching to support and assist you on your journey. All of the exercises, readings, assignments, and policies detailed below have been designed with this purpose in mind.

=== Classroom Conduct ===

We are all responsible for maintaining a classroom and laboratory environment that is safe and conducive to learning. As such, we will observe the following:

# As an LMU Lion, by the [http://studentaffairs.lmu.edu/wellness/lmucares/aboutlmucares/livethelionscode/ Lion’s code], you are pledged to join the discourse of the academy with honesty of voice and integrity of scholarship and to show respect for staff, professors, and other students.
# You are responsible for your own learning and for being a good class citizen.
# Class will start promptly on time.
# You are expected to come to class having done the assigned reading and preparatory work so that you are ready to participate in discussions and to perform the laboratory exercises.
# You are expected to bring the required materials to each class session.
# Cell phones and other ancillary devices must be turned off and put away out of sight. Your own laptop and/or tablet may be used to conduct the class exercises, '''''provided that you have confirmed with the instructor that you have the correct versions of software installed for the exercise'''''. If, however, the laptops/tablets are being used for other purposes and become distracting to you or others, you will be asked to put them away.
# All students are governed by [https://studentaffairs.lmu.edu/media/studentaffairs/osccr/documents/1920-LMU-community-standards.pdf LMU Community Standards publication]. Disruptive behavior which is persistent or significantly interferes with classroom activities may be subject to disciplinary action. A student may be referred to the Office of Student Judicial Affairs if his or her behavior constitutes a violation of the conduct code.

=== Course Website ===

This is the course web site and wiki, hosted by http://xmlpipedb.cs.lmu.edu/biodb/fall2019/. You will need to have a user account on this wiki to be able to edit the wiki and complete coursework. Updates to the course schedule and electronic copies of all handouts, assignments, and readings will be posted to this site. You will also use the site to keep an electronic lab notebook/journal for the course. In addition, students have been automatically enrolled in the course page on [http://brightspace.lmu.edu Brightspace]. The site will be used for materials that cannot be made public on this wiki, including grades.

=== Email Communication ===

At times we will communicate with the entire class using campus email systems, so it is essential that you regularly check your lion.lmu.edu email address or forward your lion account email to your preferred email address. Messages sent to the instructor at night or on the weekend will be answered the next school day.

=== Required Materials ===

==== Texts ====

There is no required text to purchase for the course; materials will be put on reserve at Hannon Library or will be available online on this wiki or [http://brightspace.lmu.edu Brightspace] site. Specific reading assignments are given on the course schedule and should be completed before coming to class.
* Assorted handouts, articles, and sample code will be distributed throughout the semester.
* Additional information is also available on the web; do not hesitate to look for further sources of information regarding the concepts, techniques, tools, and paradigms that we will discuss.

==== Materials (must be brought to each class meeting) ====

* 3-ring binder with all course handouts
* Pen, pencil, extra paper
* USB flash drive to store data
* Account for this wiki
* [http://www.github.com GitHub account]

=== Course Description ===

The disciplines of biology and computer science come together in bioinformatics, where computational tools are needed to manage and analyze the flood of data coming from new genomics technologies. Biological databases form a significant part of this young and exciting field. This course introduces students to both the biology and computer science expertise needed to understand, use, and develop biological databases. Biology topics include the fundamentals of genetics, molecular biology, and biochemistry needed to understand the data stored in biological databases, as well as the biotechnologies used to gather these data in a high-throughput manner. Computer science topics include what biological databases are, why they are important (and needed), and the challenges that arise in compiling them effectively. Biology and computer science lectures on topics that are relevant to biological databases are coupled with hands-on experience with a variety of software packages ranging from bioinformatics utilities to general-purpose database and software development tools. After learning how to use a biological database, students will be asked to build one of their own.

==== Course Objectives and Learning Outcomes ====

This course is built upon L. Dee Fink’s ''taxonomy of significant learning'', as applied to biological databases. Long after the course concludes, our hope is that:
* You understand how biological information is encoded in the genome and can apply this knowledge to a variety of biological tasks and problems
* You understand the core concepts, structure, and functions of a database, ranging from individual files to a full relational database management system, and can perform useful tasks with such data
* You show discipline and proficiency in day-to-day science and engineering best practices, such as maintaining journals and notebooks, managing your files and code, and critically evaluating scientific and technical information
* You recognize and care about how the biological and technological issues presented in this course relate to and affect society, our daily lives, and ourselves
* You have some skills and tools for “leaving your comfort zone,” flourishing outside of it, and learning more about biology and computer science on your own
* You learn how to communicate and work effectively with colleagues from different disciplines

==== University Core Curriculum ====

This course fulfills the following requirements in the University Core Curriculum:
* Integrations: Interdisciplinary Connections
* Upper Division Information Literacy Flag
* Upper Division Oral Communication Flag

=== Course Work and Grading ===

Your work in this course will be assessed in the following areas:

{| style="border: 1px dashed rgb(47, 111, 171); background: rgb(249, 249, 249); margin: 1em 0; padding: 1em" cellpadding="5ex"
| Weekly electronic lab notebook/journal assignments, individual (usually 10 points each)
| style="text-align: right" | 150
| points
|-
| Weekly electronic lab notebook/journal assignments, shared (usually 3 points each)
| style="text-align: right" | 50
| points
|-
| Oral presentations
| style="text-align: right" | 120
| points 
|-
| Final Project Deliverables (including written report)
| style="text-align: right" | 100
| points
|-
| '''Total'''
| style="text-align: right" | '''420'''
| '''points'''
|}

Final course grading scale:
94.0-100.0% A
90.0- 93.9% A-
86.0- 89.9% B+
82.0- 85.9% B
78.0- 81.9% B-
74.0- 77.9% C+
70.0- 73.9% C
67.0- 69.9% C-
60.0- 66.9% D
≤ 59.9% F

Current grades will be made available at [http://brightspace.lmu.edu Brightspace].

==== Electronic Laboratory Notebook/Journal ====

One of the most important skills you can develop as a scientist is keeping an excellent laboratory notebook. For computational research, the equivalent of the biology paper-based lab notebook is documentation of your “workflow.” For this course you will practice documentation skills by keeping an electronic lab notebook or journal. The technology we will use is this wiki. We will create and edit during the semester. The wiki software is the same one that runs Wikipedia and many other wiki sites, so proficiency gained here is easily transferable to other applications.

You will create an individual user page and make weekly entries that the instructor will read and grade. You will use the wiki to complete the assignments as well. The following guidelines apply:
* Your weekly journal entry is typically due every 12:01am on Thursday Pacific time; consult the schedule for specific due dates for each assignment.
* Each weekly assignment has an individual component and a shared component. You will earn 10 points per weekly submission for the individual journal entry and 3 points per submission for the shared journal entry. '''''Late journal entries will be accepted up to one week later for up to half credit.'''''
* The instructor will read and comment on how to improve your journal entries on your user talk pages.
* Depending on the type of assignment for that week, you may be given the opportunity to make improvements to previous journal entries as the semester progresses.
* For most weeks in the semester, you will be assigned a "homework partner". You will be expected to consult with your partner in order to complete the assignment. However, unless otherwise stated, each partner must submit his or her own work as the individual journal entry (direct copies of each other's work is not allowed).
* Generally, your journal entries will consist of:
** Your electronic laboratory notebook (workflow and other documentation) for hands-on exercises and projects
** Answers to any specific questions posed in the exercise
** Acknowledgments section (see [[Week 1]] assignment for details)
** References section (see [[Week 1]] assignment for details)
** Shared reflection on your learning, assigned readings, or ethics case studies.

==== Oral Presentations ====

You will give three oral presentations in this course. The first two will be in the format of a “Journal Club” presentation where students will present and lead discussion of research articles from the primary literature. The third will be a research presentation on your final project. '''''Because that day’s class content is dependent upon each student being ready to present and lead discussion, late journal club presentations will not be accepted. An unexcused absence from a journal club presentation will result in a grade of zero for the presentation.'''''

==== Final Group Project ====



In addition to the research presentation, the culmination of your final project will be the preparation of a written laboratory report in the style of a manuscript that could be submitted to a peer-reviewed journal. Specific instructions will be posted later in the semester. '''''The Final Written Report cannot be accepted any later than Friday, December 13 at 4:00pm. The Final Written Report must be completed to receive a passing grade in the course.'''''

==== Extra Credit ====

Students may accumulate up to 12.5 points toward their final grade in extra credit by attending Biology Department seminars and completing the seminar sheets. Each seminar attended is worth 2.5 points with up to 5 seminars (12.5 points) total. You must attend the entire seminar from start to finish and personally turn in your seminar sheet to a faculty member at the end of the seminar.

Certain non-Biology Department seminars may be approved in advance for extra credit at the instructor's discretion. To receive credit for these seminars, you must turn in a one-page '''''hard copy''''' of your summary of the seminar '''''in class, within one week''''' of the date of the seminar or they will not count as extra credit.

=== Work Load Expectations ===

In line with [http://www.lmu.edu/Assets/LMU+Credit+Hour+Policy_Final.pdf LMU’s Credit Hour Policy], the work load expectation for this course is that for every one hour (50 minutes) of classroom instruction, you will complete a minimum of two hours of out-of-class student work each week. This is a 3-unit course with 3 hours (150 minutes) of instruction per week. Thus the expectation is that you will complete 6 hours of work outside of class per week.

=== University Policy on Academic Honesty ===

Loyola Marymount University is a community dedicated to academic excellence. Academic honesty in scholarship and creative work stands at the center of LMU's academic life, and is essential for true learning and creation of knowledge to take place. As a university in the Jesuit and Marymount traditions, this community expects its members to act in accordance with the highest standards of honesty and ethics at all times. Violations of academic honesty undermine the fundamental educational mission of the University and cannot be tolerated.

Academic dishonesty will be treated as an extremely serious matter with severe consequences. '''''The minimum penalty for an instance of academic dishonesty in this course, even on a 1-point assignment or extra credit assignment, is a one-letter grade penalty in the course and a zero on the assignment.''''' It is never permissible to turn in any work that has not been authored by the student, such as work that has been copied from another student or copied from a source (including Internet) without properly acknowledging the source. It is the student's responsibility to make sure that your work meets the standard set forth in the “Academic Honesty Policy” (see http://academics.lmu.edu/honesty.) You are responsible for contacting the instructor before assignments are due to proactively resolve any questions you may have.

You are required to sign the [[Media:BIOL367_Fall2019_AcademicHonestyAgreement.pdf|Academic Honesty Agreement]] for this course.

==== Academic Honesty Resources ====
* [[Media:BIOL367_Fall2019_AcademicHonestyAgreement.pdf|Academic Honesty Agreement]]
* [http://academics.lmu.edu/media/lmuacademics/academichonesty/documents/Academic%20Honesty%20Policy%20FINAL%20Appendices--051116.pdf LMU Academic Honesty Policy and Procedures]
* [http://www.bio.davidson.edu/dept/plagiarism.html The Davidson College Department of Biology Statement on Plagiarism]
* [[Media:BIOL367_Fall2019_GuidelinesforLiteratureCitations.pdf | Guidelines for Literature Citations in a Scientific Paper]]
** [https://owl.purdue.edu/owl/research_and_citation/apa_style/apa_formatting_and_style_guide/reference_list_basic_rules.html APA Style for References]
* [http://dondi.lmu.build/share/misc/plagiarism.pdf Notes on plagiarism from an electrical engineering & computer science perspective]
* [http://libguides.lmu.edu/BIOL367 BIOL/CMSI 367 LibGuide]

=== Americans with Disabilities Act—Special Accommodations ===

Students with special needs who require reasonable modifications, special assistance, or accommodations in this course should promptly direct their request to the Disability Support Services (DSS) Office. Any student who currently has a documented disability (ADHD, Autism Spectrum Disorder, Learning, Physical, or Psychiatric) needing academic accommodations should contact the DSS Office (Daum Hall 2nd floor, 310-338-4216) as early in the semester as possible. All discussions will remain confidential. Please visit http://www.lmu.edu/dss for additional information. In addition, please schedule an appointment with the instructor early in the semester to discuss any accommodations for this course for which you have been approved.

=== Health and Wellness ===

There are many resources for student wellness on campus. A central website for students is: http://lionwellness.lmu.edu.

=== Emergency Preparedness ===

To report an emergency or suspicious activity, contact the LMU Department of Public Safety by phone (x222 or 310-338-2894) or at the nearest emergency call box. In the event of an evacuation, follow the evacuation signage throughout the building to the designated safe refuge area where you will receive further instruction from Public Safety or a Building Captain. For more safety information and preparedness tips, visit http://www.lmu.edu/emergency.

=== Revision Notice ===

If necessary, this syllabus and its contents are subject to revision; students are responsible for any changes or modifications announced in class. The most current version of this information resides on this page, the course web site at http://xmlpipedb.cs.lmu.edu/biodb/fall2019/.

Data Analysis

2019-11-22T00:12:31Z

Kdahlquist: /* Milestone 3: Getting the data ready for analysis */ add team names

{{Final Project Links}}

The role of the Data Analyst will be to apply the data analysis pipeline that you learned by analyzing the Dahlquist Lab microarray dataset to complete the analysis of a different published yeast timecourse microarray dataset. The Data Analysts are the end-users of the project, ultimately determining whether the work of the coder/designer and quality assurance members is useful to them.

== Guild Members ==

* [[Sulfiknights]]: Ivy, Marcus
* [[FunGals]]: Emma, Kaitlyn
* [[Skinny Genes]]: Aby, David

== Milestones ==

The milestones do not necessarily correspond to particular days/weeks; instead they are sets of tasks grouped together.

* Data Analysts and QA's who have a partner in their group can have a shared ''individual'' journal entry. Both students will be given the same grade and are expected to contribute equally to the electronic lab notebook.

=== Milestone 1: Annotated Bibliography ===

* The Data Analysts will work with their teams to develop an annotated bibliography of papers relating to their team's assigned paper.

=== Milestone 2: Journal Club Presentation ===

* The Data Analysts will work with their teams to create and deliver a Journal Club presentation about to their team's assigned paper.

=== Milestone 3: Getting the data ready for analysis ===

# Download and examine the microarray dataset, comparing it to the samples and experiment described in your journal club article.
#* [[Skinny Genes]]: [https://sgd-prod-upload.s3.amazonaws.com/S000204227/Barreto_2012_PMID_23039231.zip Barreto et al. (2012)]
#* [[FunGals]]: [https://sgd-prod-upload.s3.amazonaws.com/S000204415/Kitagawa_2002_PMID_12269742.zip Kitagawa et al. (2002)]
#* [[Sulfiknights]]: [https://sgd-prod-upload.s3.amazonaws.com/S000204367/Thorsen_2007_PMID_17327492.zip Thorsen et al. (2007)]
# Along with the QA's, make a "sample-data relationship table" that lists all of the samples (microarray chips), noting the treatment, time point, and replicate number.
#* Come up with consistent column headers that summarize this information
#** For example, the Dahlquist Lab microarray data used strain_LogFC_timepoint-replicate number, as in wt_LogFC_t15-1.
# Organize the data in a worksheet in an Excel workbook so that:
#* ID is in the first column
#* Data columns are to the right, in increasing chronological order, using the column header pattern you created
#* Replicates are grouped together

=== Milestone 4: ANOVA analysis ===

# Perform an ANOVA analysis of the data, as you did on [[Week 8]] for the Dahlquist lab data.
#* Note that you will need to adjust your formulas to take into account the different number of timepoints and replicates in your article's dataset.

=== Milestone 5: Clustering with stem and YEASTRACT ===

# Cluster the data with stem, as you did on [[Week 9]].
#* Note that we will make some adjustments to the GO term analysis because stem was not providing GO term names.
# Use YEASTRACT to generate a candidate gene regulatory network as you did on [[Week 9]].

=== Milestone 6: Create an input workbook for GRNmap using MS Access database ===

# Create an input workbook for GRNmap based on a Microsoft Access database that the Coder/Designer and QA's make, following protocol in [[Week 10]]
# Run GRNmap and interpret data.
# As the end-user of the Access database, the Data Analysts will provide feedback to the QAs and Coder/Designer about the usability of database.

{{Final Project Links}}

[[Category:Group Projects]]

File:Marmas Metadata Sheet.xlsx

2019-11-21T23:19:22Z

Kdahlquist: Kdahlquist uploaded a new version of File:Marmas Metadata Sheet.xlsx

Coder/Designer

2019-11-20T18:22:12Z

Kdahlquist: /* Milestone 7: Merge completed databases into a single database for the class */ note with QA's

{{Final Project Links}}

The Coder/Designer is responsible for creating the Microsoft Access database that will be used by the Data Analyst to prepare an input workbook for GRNmap for the microarray dataset they are analyzing. The Coder/Designer is also the resident expert on the technology being used—assorted software, file management, version control, and troubleshooting. He or she coordinates with Dr. Dahlquist and fellow Coders/Designers in developing the Access database and storing it on Box.

== Guild Members ==

* [[Sulfiknights]]: DeLisa Madere
* [[FunGals]]: Mike Armas
* [[Skinny Genes]]: Mihir

== Milestones ==

The milestones do not necessarily correspond to particular weeks; instead they are sets of tasks grouped together. However, Milestone 3 is a hard prerequisite for proceeding to Milestone 4, so ideally the Coder/Coder guild should finish these milestones (they require some coordination; see below) as soon as possible.

=== Milestone 1: Annotated Bibliography ===

* The Coder/Designer will work with their teams to develop an annotated bibliography of papers relating to their team's assigned paper.

=== Milestone 2: Journal Club Presentation ===

* The Coder/Designer will work with their teams to create and deliver a Journal Club presentation about to their team's assigned paper.

=== Milestone 3: Working Environment Setup ===

Coder/Designer work will require the following software/accounts. The Seaver 120 lab computers are already set up for this; this list is provided for Coders/Designers who need to work on a different computer or outside of the lab.
* Microsoft Access
* Box account (provided by LMU)
** Databases created by the teams will be kept in a [https://lmu.box.com/s/gutpb5qm0a6b2pvjn1j6moqb6y47e903 "BIOL367_Fall2019 > Final Project Database" Box folder].
** Coder/Designer guild members have rights as editor to this folder; all others in the class can only view/download.
** This folder will serve as as the version control mechanism for the Coder/Designer guild.

=== Milestone 4: Design a Database to Store Time-course Microarray Data from four sources ===

* Designer/Coders will work with the QA's to create a MS Access Database to store the yeast time-course microarray data for the dataset being analyzed by the Data Analysts.
* The starting point will be the database already used for the [[Week 10]] assignment, which can be found [https://lmu.box.com/s/kn8l6r639af683ioojqoce5w1g3z7kd2 here] on Box.
** This database is already populated with tables for the Dahlquist Lab microarray data, degradation rates from Neymotin et al. (2014), and initial guesses for production rates.
*** You may need to change the table names of these existing tables so that they make sense with the overall database design.
** You will need to add one or more expression tables for the expression data from your team's article.
*** Work with your team's QA and Data Analysts to determine appropriate column headings for the expression table.
** You will also need to create one or more tables with metadata about the other tables because now the database will contain data from multiple sources, not just one.
*** A major part of the design work will be to figure out what information needs to be in the metadata table so that queries can be easily and uniquely performed on the data.
*** Think about what information would someone need to know to be able to understand how the dataset works. Consult with the QA and Data Analysts to figure out the sample-data relationships and how that should be encoded.

=== Milestone 5: Build an individual database for your team ===

* Once the design work has been completed, you need to actually import the data into the database.
* Initially, each team will have their own database so that the QA and Data analysts can validate and use the database.

=== Milestone 6: Validation and Quality Assurance on Database ===

* The QA will perform quality assurance to make sure that the database is correct and accurate.
** In particular, the QA needs to make sure that all of the rows of data were imported into the database for the expression table(s).
** The QA will make sure that both the ID (SGD systematic name) and Standard Names are included in each expression table and are correct.
* QA's will communicate to the Coder/Designers any changes needed to the database.

=== Milestone 7: Merge completed databases into a single database for the class ===

* As a guild, the Coder/Designers will merge their separate databases into a final product.
* With the QA's finalize the [https://www.quackit.com/microsoft_access/microsoft_access_2016/howto/how_to_create_a_database_diagram_in_access_2016.cfm database schema diagram]

{{Final Project Links}}

[[Category:Group Projects]]

Quality Assurance

2019-11-20T18:21:22Z

Kdahlquist: /* Milestones */ add note about documentation

{{Final Project Links}}

The QA team member is the link between the Coder/Designer and the Data Analyst. He or she needs to know the details of the microarray dataset being analyzed so that the database being designed and populated by the Coder/Designer is correct and useful for the Data Analyst. The QA also makes sure that the data analysis steps are being performed correctly and are being correctly documented.

== Guild Members ==

* [[FunGals]]: Iliana
* [[Skinny Genes]]: Jonar, Christina
* [[Sulfiknights]]: Naomi, Joey

== Milestones ==

The milestones do not necessarily correspond to particular weeks; instead they are sets of tasks grouped together.

* Data Analysts and QA's who have a partner in their group can have a shared ''individual'' journal entry. Both students will be given the same grade and are expected to contribute equally to the electronic lab notebook.
* Detailed notes should be taken throughout consistent with reproducible research and contributing to the final deliverables.

=== Milestone 1: Annotated Bibliography ===

* The QA's will work with their teams to develop an annotated bibliography of papers relating to their team's assigned paper.

=== Milestone 2: Journal Club Presentation ===

* The QA's will work with their teams to create and deliver a Journal Club presentation about to their team's assigned paper.

=== Milestone 3: Working with Data Analysts to understand microarray dataset ===

As an overview, the QA team member is the link between the Coder/Designer and the Data Analyst. He or she needs to know the details of the microarray dataset being analyzed so that the database being designed and populated by the Coder/Designer is correct and useful for the Data Analyst. The QA will independently check that all of the data retrieved from SGD is present and accurately represented in the MS Access database. The QA will also provide assistance to the Data Analyst, making sure that the data analysis steps are being performed correctly and are being correctly documented.

Initially, the QA's will need to do the following:
* Along with the Data Analysts, download the microarray data associated with your group's article.
** [https://sgd-prod-upload.s3.amazonaws.com/S000204227/Barreto_2012_PMID_23039231.zip Barreto et al. (2012)]
** [https://sgd-prod-upload.s3.amazonaws.com/S000204415/Kitagawa_2002_PMID_12269742.zip Kitagawa et al. (2002)]
** [https://sgd-prod-upload.s3.amazonaws.com/S000204367/Thorsen_2007_PMID_17327492.zip Thorsen et al. (2007)]
** Along with the Data Analysts, make a "sample-data relationship table" that lists all of the samples (microarray chips), noting the treatment, time point, and replicate number.
** Are all the samples described in the paper in the dataset?
** Are all the samples in the dataset described in the paper?
* Come up with consistent column headers that summarize this information
** For example, the Dahlquist Lab microarray data used strain_LogFC_timepoint-replicate number, as in wt_LogFC_t15-1.
* Organize the data in a worksheet in an Excel workbook so that:
* ID is in the first column
* Data columns are to the right, in increasing chronological order, using the column header pattern you created
* Replicates are grouped together

=== Milestone 4: Making sure expression data has both Sytematic Name and Standard Name ID's ===

* The design of the expression tables in the final database will need both an ID field (yeast systematic name) and Standard Name fields.
* You will need to check the IDs in the expression data and potentially populate one or both of these fields.
* One way to do this is use the [http://www.yeastract.com/formorftogene.php "ORF List <-> Gene List"] tool at YEASTRACT.
* The [http://llama.mshri.on.ca/synergizer/translate/ Synergizer] website may also be helpful.
* Here is a [https://rdrr.io/bioc/ClusterJudge/man/convert_Yeast_SGDId_2_systematic.html Bioconductor package] for it, too.

=== Milestone 5: Work with Coder/Designers to Design a Database to Store Time-course Microarray Data from four sources ===

* Databases created by the teams will be kept in a [https://lmu.box.com/s/gutpb5qm0a6b2pvjn1j6moqb6y47e903 "BIOL367_Fall2019 > Final Project Database" Box folder].
* Coder/Designer guild members have rights as editor to this folder; all others in the class can only view/download.
* This folder will serve as as the version control mechanism for the Coder/Designer guild.
* Designer/Coders will work with the QA's to create a MS Access Database to store the yeast time-course microarray data for the dataset being analyzed by the Data Analysts.
* The starting point will be the database already used for the [[Week 10]] assignment, which can be found [https://lmu.box.com/s/kn8l6r639af683ioojqoce5w1g3z7kd2 here] on Box.
** This database is already populated with tables for the Dahlquist Lab microarray data, degradation rates from Neymotin et al. (2014), and initial guesses for production rates.
*** You may need to change the table names of these existing tables so that they make sense with the overall database design.
** You will need to add one or more expression tables for the expression data from your team's article.
*** Work with your team's QA and Data Analysts to determine appropriate column headings for the expression table.
** You will also need to create one or more tables with metadata about the other tables because now the database will contain data from multiple sources, not just one.
*** A major part of the design work will be to figure out what information needs to be in the metadata table so that queries can be easily and uniquely performed on the data.

=== Milestone 6: Validation and Quality Assurance on Database ===

* After the Access database is built by the Coder/Designer, the QA will perform quality assurance to make sure that the database is correct and accurate.
** In particular, the QA needs to make sure that all of the rows of data were imported into the database for the expression table(s).
** The QA will make sure that both the ID (SGD systematic name) and Standard Names are included in each expression table and are correct.
* QA's will communicate to the Coder/Designers any changes needed to the database.

=== Milestone 7: Final Documentation ===

* With the Coder/Designer, finalize the [https://www.quackit.com/microsoft_access/microsoft_access_2016/howto/how_to_create_a_database_diagram_in_access_2016.cfm database schema diagram]

{{Final Project Links}}

[[Category:Group Projects]]

Quality Assurance

2019-11-20T18:19:44Z

Kdahlquist: /* Milestone 6: Validation and Quality Assurance on Database */ add finalize and schema diagram

{{Final Project Links}}

The QA team member is the link between the Coder/Designer and the Data Analyst. He or she needs to know the details of the microarray dataset being analyzed so that the database being designed and populated by the Coder/Designer is correct and useful for the Data Analyst. The QA also makes sure that the data analysis steps are being performed correctly and are being correctly documented.

== Guild Members ==

* [[FunGals]]: Iliana
* [[Skinny Genes]]: Jonar, Christina
* [[Sulfiknights]]: Naomi, Joey

== Milestones ==

The milestones do not necessarily correspond to particular weeks; instead they are sets of tasks grouped together.

* Data Analysts and QA's who have a partner in their group can have a shared ''individual'' journal entry. Both students will be given the same grade and are expected to contribute equally to the electronic lab notebook.

=== Milestone 1: Annotated Bibliography ===

* The QA's will work with their teams to develop an annotated bibliography of papers relating to their team's assigned paper.

=== Milestone 2: Journal Club Presentation ===

* The QA's will work with their teams to create and deliver a Journal Club presentation about to their team's assigned paper.

=== Milestone 3: Working with Data Analysts to understand microarray dataset ===

As an overview, the QA team member is the link between the Coder/Designer and the Data Analyst. He or she needs to know the details of the microarray dataset being analyzed so that the database being designed and populated by the Coder/Designer is correct and useful for the Data Analyst. The QA will independently check that all of the data retrieved from SGD is present and accurately represented in the MS Access database. The QA will also provide assistance to the Data Analyst, making sure that the data analysis steps are being performed correctly and are being correctly documented.

Initially, the QA's will need to do the following:
* Along with the Data Analysts, download the microarray data associated with your group's article.
** [https://sgd-prod-upload.s3.amazonaws.com/S000204227/Barreto_2012_PMID_23039231.zip Barreto et al. (2012)]
** [https://sgd-prod-upload.s3.amazonaws.com/S000204415/Kitagawa_2002_PMID_12269742.zip Kitagawa et al. (2002)]
** [https://sgd-prod-upload.s3.amazonaws.com/S000204367/Thorsen_2007_PMID_17327492.zip Thorsen et al. (2007)]
** Along with the Data Analysts, make a "sample-data relationship table" that lists all of the samples (microarray chips), noting the treatment, time point, and replicate number.
** Are all the samples described in the paper in the dataset?
** Are all the samples in the dataset described in the paper?
* Come up with consistent column headers that summarize this information
** For example, the Dahlquist Lab microarray data used strain_LogFC_timepoint-replicate number, as in wt_LogFC_t15-1.
* Organize the data in a worksheet in an Excel workbook so that:
* ID is in the first column
* Data columns are to the right, in increasing chronological order, using the column header pattern you created
* Replicates are grouped together

=== Milestone 4: Making sure expression data has both Sytematic Name and Standard Name ID's ===

* The design of the expression tables in the final database will need both an ID field (yeast systematic name) and Standard Name fields.
* You will need to check the IDs in the expression data and potentially populate one or both of these fields.
* One way to do this is use the [http://www.yeastract.com/formorftogene.php "ORF List <-> Gene List"] tool at YEASTRACT.
* The [http://llama.mshri.on.ca/synergizer/translate/ Synergizer] website may also be helpful.
* Here is a [https://rdrr.io/bioc/ClusterJudge/man/convert_Yeast_SGDId_2_systematic.html Bioconductor package] for it, too.

=== Milestone 5: Work with Coder/Designers to Design a Database to Store Time-course Microarray Data from four sources ===

* Databases created by the teams will be kept in a [https://lmu.box.com/s/gutpb5qm0a6b2pvjn1j6moqb6y47e903 "BIOL367_Fall2019 > Final Project Database" Box folder].
* Coder/Designer guild members have rights as editor to this folder; all others in the class can only view/download.
* This folder will serve as as the version control mechanism for the Coder/Designer guild.
* Designer/Coders will work with the QA's to create a MS Access Database to store the yeast time-course microarray data for the dataset being analyzed by the Data Analysts.
* The starting point will be the database already used for the [[Week 10]] assignment, which can be found [https://lmu.box.com/s/kn8l6r639af683ioojqoce5w1g3z7kd2 here] on Box.
** This database is already populated with tables for the Dahlquist Lab microarray data, degradation rates from Neymotin et al. (2014), and initial guesses for production rates.
*** You may need to change the table names of these existing tables so that they make sense with the overall database design.
** You will need to add one or more expression tables for the expression data from your team's article.
*** Work with your team's QA and Data Analysts to determine appropriate column headings for the expression table.
** You will also need to create one or more tables with metadata about the other tables because now the database will contain data from multiple sources, not just one.
*** A major part of the design work will be to figure out what information needs to be in the metadata table so that queries can be easily and uniquely performed on the data.

=== Milestone 6: Validation and Quality Assurance on Database ===

* After the Access database is built by the Coder/Designer, the QA will perform quality assurance to make sure that the database is correct and accurate.
** In particular, the QA needs to make sure that all of the rows of data were imported into the database for the expression table(s).
** The QA will make sure that both the ID (SGD systematic name) and Standard Names are included in each expression table and are correct.
* QA's will communicate to the Coder/Designers any changes needed to the database.

=== Milestone 7: Final Documentation ===

* With the Coder/Designer, finalize the [https://www.quackit.com/microsoft_access/microsoft_access_2016/howto/how_to_create_a_database_diagram_in_access_2016.cfm database schema diagram]

{{Final Project Links}}

[[Category:Group Projects]]

Coder/Designer

2019-11-20T18:18:45Z

Kdahlquist: /* Milestone 7: Merge completed databases into a single database for the class */ add database schema diagram

{{Final Project Links}}

The Coder/Designer is responsible for creating the Microsoft Access database that will be used by the Data Analyst to prepare an input workbook for GRNmap for the microarray dataset they are analyzing. The Coder/Designer is also the resident expert on the technology being used—assorted software, file management, version control, and troubleshooting. He or she coordinates with Dr. Dahlquist and fellow Coders/Designers in developing the Access database and storing it on Box.

== Guild Members ==

* [[Sulfiknights]]: DeLisa Madere
* [[FunGals]]: Mike Armas
* [[Skinny Genes]]: Mihir

== Milestones ==

The milestones do not necessarily correspond to particular weeks; instead they are sets of tasks grouped together. However, Milestone 3 is a hard prerequisite for proceeding to Milestone 4, so ideally the Coder/Coder guild should finish these milestones (they require some coordination; see below) as soon as possible.

=== Milestone 1: Annotated Bibliography ===

* The Coder/Designer will work with their teams to develop an annotated bibliography of papers relating to their team's assigned paper.

=== Milestone 2: Journal Club Presentation ===

* The Coder/Designer will work with their teams to create and deliver a Journal Club presentation about to their team's assigned paper.

=== Milestone 3: Working Environment Setup ===

Coder/Designer work will require the following software/accounts. The Seaver 120 lab computers are already set up for this; this list is provided for Coders/Designers who need to work on a different computer or outside of the lab.
* Microsoft Access
* Box account (provided by LMU)
** Databases created by the teams will be kept in a [https://lmu.box.com/s/gutpb5qm0a6b2pvjn1j6moqb6y47e903 "BIOL367_Fall2019 > Final Project Database" Box folder].
** Coder/Designer guild members have rights as editor to this folder; all others in the class can only view/download.
** This folder will serve as as the version control mechanism for the Coder/Designer guild.

=== Milestone 4: Design a Database to Store Time-course Microarray Data from four sources ===

* Designer/Coders will work with the QA's to create a MS Access Database to store the yeast time-course microarray data for the dataset being analyzed by the Data Analysts.
* The starting point will be the database already used for the [[Week 10]] assignment, which can be found [https://lmu.box.com/s/kn8l6r639af683ioojqoce5w1g3z7kd2 here] on Box.
** This database is already populated with tables for the Dahlquist Lab microarray data, degradation rates from Neymotin et al. (2014), and initial guesses for production rates.
*** You may need to change the table names of these existing tables so that they make sense with the overall database design.
** You will need to add one or more expression tables for the expression data from your team's article.
*** Work with your team's QA and Data Analysts to determine appropriate column headings for the expression table.
** You will also need to create one or more tables with metadata about the other tables because now the database will contain data from multiple sources, not just one.
*** A major part of the design work will be to figure out what information needs to be in the metadata table so that queries can be easily and uniquely performed on the data.
*** Think about what information would someone need to know to be able to understand how the dataset works. Consult with the QA and Data Analysts to figure out the sample-data relationships and how that should be encoded.

=== Milestone 5: Build an individual database for your team ===

* Once the design work has been completed, you need to actually import the data into the database.
* Initially, each team will have their own database so that the QA and Data analysts can validate and use the database.

=== Milestone 6: Validation and Quality Assurance on Database ===

* The QA will perform quality assurance to make sure that the database is correct and accurate.
** In particular, the QA needs to make sure that all of the rows of data were imported into the database for the expression table(s).
** The QA will make sure that both the ID (SGD systematic name) and Standard Names are included in each expression table and are correct.
* QA's will communicate to the Coder/Designers any changes needed to the database.

=== Milestone 7: Merge completed databases into a single database for the class ===

* As a guild, the Coder/Designers will merge their separate databases into a final product.
* Finalize the [https://www.quackit.com/microsoft_access/microsoft_access_2016/howto/how_to_create_a_database_diagram_in_access_2016.cfm database schema diagram]

{{Final Project Links}}

[[Category:Group Projects]]

Final Project Deliverables

2019-11-20T18:17:59Z

Kdahlquist: /* Deliverables Checklist */ add link to instructions for schema diagram

{{Final Project Links}}

== Deliverables Checklist ==

# Organized Team deliverables wiki page (or other media (CD or flash drive) with table of contents)
# Group Report (''.doc'', ''.docx'' or ''.pdf'' file)
# Individual statements of work, assessments, reflections (wiki page, ''.doc'', ''.docx'', ''.pdf'', or e-mailed to Dr. Dahlquist)
# Group PowerPoint presentation (given on Tuesday, December 10, ''.ppt'', ''.pptx'' or ''.pdf'' file)
# Sample-data relationship table in Excel (''.xlsx'')
# Excel spreadsheet with ANOVA results/stem formatting (''.xlsx'')
# PowerPoint of ANOVA table, screenshots of stem results (''.pptx''), screenshot of black and white GRNsight input network and colored GRNsight output networks
# Gene List and GO List files from each significant profile (''.txt'' compressed together in a ''.zip'' file)
# YEASTRACT "rank by TF" results (''.xlsx'')
# GRNmap input workbook (with network adjacency matrix, ''.xlsx'')
# GRNmap output workbook (''.xlsx'') and output plots (''.jpg'') zipped together
# MS Access database, unified by the three teams with expression tables and metadata table(s) created (''.accdb'')
# ReadMe for the database that describes the design of the database, references the sources of the data, and has a [https://www.quackit.com/microsoft_access/microsoft_access_2016/howto/how_to_create_a_database_diagram_in_access_2016.cfm database schema diagram] (''.doc'', ''.docx'', ''.pdf'')
# Query design for populating a GRNmap input workbook from the database (screenshot of MS Access, or SQL code, ''.txt'')
# Electronic notebook corresponding to these the microarray results files ([[Week 12/13]] and [[Week 15]]) to support ''reproducible research'' so that all manipulations of the data and files are documented so that someone else could begin with your starting file, follow the protocol, and obtain your results.

=== Grading ===

* The Group Report is worth 50 points.
* The Group PowerPoint presentation is worth 50 points.
* All other deliverables listed above, together, are worth 50 points.

== Group Report ==

* The report should be written with contributions from all group members.
* Submit as ''.doc'', ''.docx'' or ''.pdf'' file.

=== Style Sheet ===

Use the following guidelines when formatting your report:
* 2.54 cm (1 in) margins on all sides
* Double-spaced
* 12 point Times/Times New Roman font
* Number the pages on the lower-right corner
* Use left justification (“jagged” on the right side)

=== Title Page ===

Include the following information in a standalone title page:
* A descriptive title for your project
** The function of the title is to identify the main result or take-home message of the paper. It should be as specific as possible. It can be a phrase or a sentence. What is the main result of your paper that you want to convey with the title?
* The names of the team members (with middle initials)
* The course number and title of the class
* The date of submission

=== Introduction ===

The introduction gives the background information necessary to understand your report and should be '''2-3''' pages long (double-spaced). The introduction should be in the form of a logical argument that “funnels” from broad to narrow:

<gallery mode="nolines" widths=322px heights=256px>
Funnel.jpg
</gallery>

* States importance of the problem
* States what is known about the problem
** Give an overview of what is known about the regulation of the transcriptional response in yeast from your team's journal article and the annotated bibliography your team developed.
** Discuss how you will approach this by re-analyzing the yeast microarray data from your article.
* States what is unknown about the problem
** Which transcription factors belong in the gene regulatory network that controls the response in yeast is still unknown.
* States clues that suggest how to approach the unknown
** Introduce GRNmap and GRNsight as the answer to this problem.
* States the question the paper is trying to address
** In this case you want to discover new information about the microarray data using GRNmap and GRNsight.
* State the approach
** Creating an MS Access database to facilitate making a GRNmap input workbook that can be used by your group and other scientists in the future.

=== Combined Methods/Results/Discussion section ===

This section will summarize the entire workflow and findings for the project with contributions from all team members (Data Analyst, QA, and Coder/Designers). Create a combined flow chart of the tasks/milestones of each of the team members and then briefly describe the flow chart in the text.
* Number each of the figures sequentially and number each of the tables sequentially in order from first mention in the text. You can either embed your figures and tables in the appropriate place in the text or put them all at the end. Do not mix both styles, however.
* Write a descriptive legend for each figure and table that briefly states what the figure/table is and gives a brief key to any labels and abbreviations.

==== Data Analyst ====

* Table of ANOVA results from the [[Week 8]], discussing the interpretation of the p values.
** With an ANOVA p value < 0.05, are there more than 5% of the genes with a significant change in gene expression at any timepoint?
** Compare with what the authors of the paper considered a meaningful gene expression change.
* From the STEM analyis, include as figures the overall results (the screenshot showing all of the clusters) and then focus on the ones you interpreted for the [[Week 10]] journal assignment.
** Were there clusters shown in your journal article? How do they compare with the ones you got?
** Include a table showing the GO results for that cluster (just the narrowed down list of terms that you have interpreted).
** Discuss what the p values for the cluster and for the GO term list mean.
** Discuss the biological interpretation of your GO terms.
** Were there GO/MIPS terms in your article? Compare your terms with the article.
* Include a table that lists the transcription factors in your final network and their enrichment p value from YEASTRACT.
** Describe how and why you and your partner chose these transcription factors for your network.
** Include a figure (screenshot) of the unweighted networks visualized with GRNsight.
** What transcription factors were mentioned in your article? Are yours the same or different?
* GRNmap results
** Show the GRNsight visualization (screenshot) of the weighted networks, making sure that the genes are placed in the same relative location as each other an as the unweighted network figure.
** Provide the LSE:minLSE ratio
** Provide a table of the weights, P's, and b's
** Organize and show the individual expression plots
** Interpret the results of the model
*** What seem to be the most important transcription factors in the network? How does that compare with the journal article?

==== Coder/Designers ====

* Give a narrative description of the database, including the design of the expression tables and metadata table(s).
* Provide a database schema.

==== QA ====

* Describe the QA process for the database, noting any issues that were encountered, and stating whether data are complete or not.
** Refer to the sample-data relationship table to discuss how microarray data format was regularized
** Discuss whether all yeast gene IDs were imported into the database and any formatting issues

=== Conclusions ===

* Write a '''1-2''' page conclusion that summarizes the overall project and your findings.
** Overall, what have you learned about the biological phenomenon described in your journal club article?
** How does the new database facilitate the data analysis?
** What future directions would you take if you were to continue this project?
** Relate the results of your project to the papers you presented for journal club and included in your bibliography in [[Week 11]].
*** Did you discover anything new that wasn't reported in the journal club paper (Data Analyst/QA)?

=== Acknowledgments ===

Write a short paragraph acknowledging the assistance of anyone who is not a member of your team.

=== References ===

* This section lists all of the references cited in the text of the report (and only those references cited in the paper). Follow the [[Media: BIOL367_Fall2019_GuidelinesforLiteratureCitations.pdf | Guidelines for Literature Citations in a Scientific Paper]] handout for general principles.
* Remember that you need to cite anything for which you are not the original source. Generally, in the introduction, you should aim for a minimum of two in-text citations per paragraph. You may reference the course web site using the appropriate format for a web reference.
* List your references in alphabetical order by first author using the [https://owl.purdue.edu/owl/research_and_citation/apa_style/apa_formatting_and_style_guide/reference_list_basic_rules.html APA style] that you have been using throughout the semester. Note that the proper format must be followed for both articles and websites, just URLs are not sufficient.

== PowerPoint Presentation ==

Each team of students will prepare and give a 20 minute PowerPoint presentation to report the results of their project on Tuesday, December 10 at 2:00-4:00 PM.
* Please follow the [http://brightspace.lmu.edu Presentation Guidelines on Brightspace] for how to format your slides.
* You will need to prepare ~20 slides (assume 1 slide per minute of presentation) and include the following content:
** Title slide that gives the main take-home message as the title of your presentation, the authors, date, and venue (course number and title).
** Outline slide that is a summary of take-home messages of your talk (should mirror your conclusion slide)
** The body of your talk (organized in a logical flow, not necessarily in the order given below).
*** Introduce the importance/significance of the problem and give background on the experiment in your journal article. You can draw from the your journal club presentation. This should follow the logic of the Introduction section of your group paper.
*** Show a combined flow chart of the tasks/milestones completed by your group (see Materials and Methods of group paper).
*** Show the table of ANOVA results.
*** Show the screenshot of the overall clustering results and the cluster you focused on.
*** Show a table of the GO results from that cluster, giving an interpretation.
*** Show the table of regulatory transcription factors in your network and their p values for enrichment.
*** Show the unweighted and weighted networks in GRNsight (with the genes arranged in the same way in each figure)
*** Database schema diagram for the MS Access database
** Conclusion slide that mirrors your outline
** Future directions
** Acknowledgments
** References
* '''''Your PowerPoint slides must be uploaded to the wiki and linked to from your individual journal page and your team page by 12:01am, Tuesday, December 10.'''''
** You can update your slides before your presentation, but we will be grading the ones you upload by the deadline.
* Your presentation (both the slides and the oral presentation) will be evaluated by the instructors using the [[Presentation Rubric]].
* Your presentation will also be evaluated by your fellow classmates (anonymously) who will answer the following questions:
*# What is the speaker's take-home message (one short sentence)?
*# What are the best points about the presentation's organization, visuals, and delivery? Please give at least 2 specific examples.
*# What points need improvement? Please give at least 2 specific examples.
* We expect that you will take the feedback from your previous presentation into account when doing this presentation.

== Individual Assessment and Reflection ==

Each person on the team will complete an assessment and reflection ''individually''. If you are comfortable with making this assessment publicly available, you may write it up as a wiki page or as a Word document uploaded to your group deliveables page. If you prefer to communicate your assessment privately, then email this to both Drs. Dahlquist and Dionisio.

=== Statement of Work ===

* Describe exactly what you did on the project.
* Provide references or links to artifacts of your work, such as:
** Wiki pages
** Other files or documents
** Code or scripts

=== Assessment of Project ===

* Give an objective assessment of the success of your project workflow and teamwork.
* What worked and what didn't work?
* What would you do differently if you could do it all over again?
* Evaluate your team’s portion of the Final Project and Group Report in the following areas:
*# Content: What is the quality of the work?
*# Organization: Comment on the organization of the project and of your group's wiki pages.
*# Completeness: Did your team achieve all of the project objectives? Why or why not?

=== Reflection on the Process ===

* What did you learn?
** With your head (biological or computer science principles)
** With your heart (personal qualities and teamwork qualities that make things work or not work)?
** With your hands (technical skills)?
* What lesson will you take away from this project that you will still use a year from now?

{{Final Project Links}}

[[Category:Group Projects]]

Final Project Deliverables

2019-11-20T18:12:47Z

Kdahlquist: /* QA */ flesh out QA a little more

{{Final Project Links}}

== Deliverables Checklist ==

# Organized Team deliverables wiki page (or other media (CD or flash drive) with table of contents)
# Group Report (''.doc'', ''.docx'' or ''.pdf'' file)
# Individual statements of work, assessments, reflections (wiki page, ''.doc'', ''.docx'', ''.pdf'', or e-mailed to Dr. Dahlquist)
# Group PowerPoint presentation (given on Tuesday, December 10, ''.ppt'', ''.pptx'' or ''.pdf'' file)
# Sample-data relationship table in Excel (''.xlsx'')
# Excel spreadsheet with ANOVA results/stem formatting (''.xlsx'')
# PowerPoint of ANOVA table, screenshots of stem results (''.pptx''), screenshot of black and white GRNsight input network and colored GRNsight output networks
# Gene List and GO List files from each significant profile (''.txt'' compressed together in a ''.zip'' file)
# YEASTRACT "rank by TF" results (''.xlsx'')
# GRNmap input workbook (with network adjacency matrix, ''.xlsx'')
# GRNmap output workbook (''.xlsx'') and output plots (''.jpg'') zipped together
# MS Access database, unified by the three teams with expression tables and metadata table(s) created (''.accdb'')
# ReadMe for the database that describes the design of the database, references the sources of the data, and has a database schema diagram (''.doc'', ''.docx'', ''.pdf'')
# Query design for populating a GRNmap input workbook from the database (screenshot of MS Access, or SQL code, ''.txt'')
# Electronic notebook corresponding to these the microarray results files ([[Week 12/13]] and [[Week 15]]) to support ''reproducible research'' so that all manipulations of the data and files are documented so that someone else could begin with your starting file, follow the protocol, and obtain your results.

=== Grading ===

* The Group Report is worth 50 points.
* The Group PowerPoint presentation is worth 50 points.
* All other deliverables listed above, together, are worth 50 points.

== Group Report ==

* The report should be written with contributions from all group members.
* Submit as ''.doc'', ''.docx'' or ''.pdf'' file.

=== Style Sheet ===

Use the following guidelines when formatting your report:
* 2.54 cm (1 in) margins on all sides
* Double-spaced
* 12 point Times/Times New Roman font
* Number the pages on the lower-right corner
* Use left justification (“jagged” on the right side)

=== Title Page ===

Include the following information in a standalone title page:
* A descriptive title for your project
** The function of the title is to identify the main result or take-home message of the paper. It should be as specific as possible. It can be a phrase or a sentence. What is the main result of your paper that you want to convey with the title?
* The names of the team members (with middle initials)
* The course number and title of the class
* The date of submission

=== Introduction ===

The introduction gives the background information necessary to understand your report and should be '''2-3''' pages long (double-spaced). The introduction should be in the form of a logical argument that “funnels” from broad to narrow:

<gallery mode="nolines" widths=322px heights=256px>
Funnel.jpg
</gallery>

* States importance of the problem
* States what is known about the problem
** Give an overview of what is known about the regulation of the transcriptional response in yeast from your team's journal article and the annotated bibliography your team developed.
** Discuss how you will approach this by re-analyzing the yeast microarray data from your article.
* States what is unknown about the problem
** Which transcription factors belong in the gene regulatory network that controls the response in yeast is still unknown.
* States clues that suggest how to approach the unknown
** Introduce GRNmap and GRNsight as the answer to this problem.
* States the question the paper is trying to address
** In this case you want to discover new information about the microarray data using GRNmap and GRNsight.
* State the approach
** Creating an MS Access database to facilitate making a GRNmap input workbook that can be used by your group and other scientists in the future.

=== Combined Methods/Results/Discussion section ===

This section will summarize the entire workflow and findings for the project with contributions from all team members (Data Analyst, QA, and Coder/Designers). Create a combined flow chart of the tasks/milestones of each of the team members and then briefly describe the flow chart in the text.
* Number each of the figures sequentially and number each of the tables sequentially in order from first mention in the text. You can either embed your figures and tables in the appropriate place in the text or put them all at the end. Do not mix both styles, however.
* Write a descriptive legend for each figure and table that briefly states what the figure/table is and gives a brief key to any labels and abbreviations.

==== Data Analyst ====

* Table of ANOVA results from the [[Week 8]], discussing the interpretation of the p values.
** With an ANOVA p value < 0.05, are there more than 5% of the genes with a significant change in gene expression at any timepoint?
** Compare with what the authors of the paper considered a meaningful gene expression change.
* From the STEM analyis, include as figures the overall results (the screenshot showing all of the clusters) and then focus on the ones you interpreted for the [[Week 10]] journal assignment.
** Were there clusters shown in your journal article? How do they compare with the ones you got?
** Include a table showing the GO results for that cluster (just the narrowed down list of terms that you have interpreted).
** Discuss what the p values for the cluster and for the GO term list mean.
** Discuss the biological interpretation of your GO terms.
** Were there GO/MIPS terms in your article? Compare your terms with the article.
* Include a table that lists the transcription factors in your final network and their enrichment p value from YEASTRACT.
** Describe how and why you and your partner chose these transcription factors for your network.
** Include a figure (screenshot) of the unweighted networks visualized with GRNsight.
** What transcription factors were mentioned in your article? Are yours the same or different?
* GRNmap results
** Show the GRNsight visualization (screenshot) of the weighted networks, making sure that the genes are placed in the same relative location as each other an as the unweighted network figure.
** Provide the LSE:minLSE ratio
** Provide a table of the weights, P's, and b's
** Organize and show the individual expression plots
** Interpret the results of the model
*** What seem to be the most important transcription factors in the network? How does that compare with the journal article?

==== Coder/Designers ====

* Give a narrative description of the database, including the design of the expression tables and metadata table(s).
* Provide a database schema.

==== QA ====

* Describe the QA process for the database, noting any issues that were encountered, and stating whether data are complete or not.
** Refer to the sample-data relationship table to discuss how microarray data format was regularized
** Discuss whether all yeast gene IDs were imported into the database and any formatting issues

=== Conclusions ===

* Write a '''1-2''' page conclusion that summarizes the overall project and your findings.
** Overall, what have you learned about the biological phenomenon described in your journal club article?
** How does the new database facilitate the data analysis?
** What future directions would you take if you were to continue this project?
** Relate the results of your project to the papers you presented for journal club and included in your bibliography in [[Week 11]].
*** Did you discover anything new that wasn't reported in the journal club paper (Data Analyst/QA)?

=== Acknowledgments ===

Write a short paragraph acknowledging the assistance of anyone who is not a member of your team.

=== References ===

* This section lists all of the references cited in the text of the report (and only those references cited in the paper). Follow the [[Media: BIOL367_Fall2019_GuidelinesforLiteratureCitations.pdf | Guidelines for Literature Citations in a Scientific Paper]] handout for general principles.
* Remember that you need to cite anything for which you are not the original source. Generally, in the introduction, you should aim for a minimum of two in-text citations per paragraph. You may reference the course web site using the appropriate format for a web reference.
* List your references in alphabetical order by first author using the [https://owl.purdue.edu/owl/research_and_citation/apa_style/apa_formatting_and_style_guide/reference_list_basic_rules.html APA style] that you have been using throughout the semester. Note that the proper format must be followed for both articles and websites, just URLs are not sufficient.

== PowerPoint Presentation ==

Each team of students will prepare and give a 20 minute PowerPoint presentation to report the results of their project on Tuesday, December 10 at 2:00-4:00 PM.
* Please follow the [http://brightspace.lmu.edu Presentation Guidelines on Brightspace] for how to format your slides.
* You will need to prepare ~20 slides (assume 1 slide per minute of presentation) and include the following content:
** Title slide that gives the main take-home message as the title of your presentation, the authors, date, and venue (course number and title).
** Outline slide that is a summary of take-home messages of your talk (should mirror your conclusion slide)
** The body of your talk (organized in a logical flow, not necessarily in the order given below).
*** Introduce the importance/significance of the problem and give background on the experiment in your journal article. You can draw from the your journal club presentation. This should follow the logic of the Introduction section of your group paper.
*** Show a combined flow chart of the tasks/milestones completed by your group (see Materials and Methods of group paper).
*** Show the table of ANOVA results.
*** Show the screenshot of the overall clustering results and the cluster you focused on.
*** Show a table of the GO results from that cluster, giving an interpretation.
*** Show the table of regulatory transcription factors in your network and their p values for enrichment.
*** Show the unweighted and weighted networks in GRNsight (with the genes arranged in the same way in each figure)
*** Database schema diagram for the MS Access database
** Conclusion slide that mirrors your outline
** Future directions
** Acknowledgments
** References
* '''''Your PowerPoint slides must be uploaded to the wiki and linked to from your individual journal page and your team page by 12:01am, Tuesday, December 10.'''''
** You can update your slides before your presentation, but we will be grading the ones you upload by the deadline.
* Your presentation (both the slides and the oral presentation) will be evaluated by the instructors using the [[Presentation Rubric]].
* Your presentation will also be evaluated by your fellow classmates (anonymously) who will answer the following questions:
*# What is the speaker's take-home message (one short sentence)?
*# What are the best points about the presentation's organization, visuals, and delivery? Please give at least 2 specific examples.
*# What points need improvement? Please give at least 2 specific examples.
* We expect that you will take the feedback from your previous presentation into account when doing this presentation.

== Individual Assessment and Reflection ==

Each person on the team will complete an assessment and reflection ''individually''. If you are comfortable with making this assessment publicly available, you may write it up as a wiki page or as a Word document uploaded to your group deliveables page. If you prefer to communicate your assessment privately, then email this to both Drs. Dahlquist and Dionisio.

=== Statement of Work ===

* Describe exactly what you did on the project.
* Provide references or links to artifacts of your work, such as:
** Wiki pages
** Other files or documents
** Code or scripts

=== Assessment of Project ===

* Give an objective assessment of the success of your project workflow and teamwork.
* What worked and what didn't work?
* What would you do differently if you could do it all over again?
* Evaluate your team’s portion of the Final Project and Group Report in the following areas:
*# Content: What is the quality of the work?
*# Organization: Comment on the organization of the project and of your group's wiki pages.
*# Completeness: Did your team achieve all of the project objectives? Why or why not?

=== Reflection on the Process ===

* What did you learn?
** With your head (biological or computer science principles)
** With your heart (personal qualities and teamwork qualities that make things work or not work)?
** With your hands (technical skills)?
* What lesson will you take away from this project that you will still use a year from now?

{{Final Project Links}}

[[Category:Group Projects]]

Final Project Deliverables

2019-11-20T18:11:14Z

Kdahlquist: /* Deliverables Checklist */ add sample-data relationship table

{{Final Project Links}}

== Deliverables Checklist ==

# Organized Team deliverables wiki page (or other media (CD or flash drive) with table of contents)
# Group Report (''.doc'', ''.docx'' or ''.pdf'' file)
# Individual statements of work, assessments, reflections (wiki page, ''.doc'', ''.docx'', ''.pdf'', or e-mailed to Dr. Dahlquist)
# Group PowerPoint presentation (given on Tuesday, December 10, ''.ppt'', ''.pptx'' or ''.pdf'' file)
# Sample-data relationship table in Excel (''.xlsx'')
# Excel spreadsheet with ANOVA results/stem formatting (''.xlsx'')
# PowerPoint of ANOVA table, screenshots of stem results (''.pptx''), screenshot of black and white GRNsight input network and colored GRNsight output networks
# Gene List and GO List files from each significant profile (''.txt'' compressed together in a ''.zip'' file)
# YEASTRACT "rank by TF" results (''.xlsx'')
# GRNmap input workbook (with network adjacency matrix, ''.xlsx'')
# GRNmap output workbook (''.xlsx'') and output plots (''.jpg'') zipped together
# MS Access database, unified by the three teams with expression tables and metadata table(s) created (''.accdb'')
# ReadMe for the database that describes the design of the database, references the sources of the data, and has a database schema diagram (''.doc'', ''.docx'', ''.pdf'')
# Query design for populating a GRNmap input workbook from the database (screenshot of MS Access, or SQL code, ''.txt'')
# Electronic notebook corresponding to these the microarray results files ([[Week 12/13]] and [[Week 15]]) to support ''reproducible research'' so that all manipulations of the data and files are documented so that someone else could begin with your starting file, follow the protocol, and obtain your results.

=== Grading ===

* The Group Report is worth 50 points.
* The Group PowerPoint presentation is worth 50 points.
* All other deliverables listed above, together, are worth 50 points.

== Group Report ==

* The report should be written with contributions from all group members.
* Submit as ''.doc'', ''.docx'' or ''.pdf'' file.

=== Style Sheet ===

Use the following guidelines when formatting your report:
* 2.54 cm (1 in) margins on all sides
* Double-spaced
* 12 point Times/Times New Roman font
* Number the pages on the lower-right corner
* Use left justification (“jagged” on the right side)

=== Title Page ===

Include the following information in a standalone title page:
* A descriptive title for your project
** The function of the title is to identify the main result or take-home message of the paper. It should be as specific as possible. It can be a phrase or a sentence. What is the main result of your paper that you want to convey with the title?
* The names of the team members (with middle initials)
* The course number and title of the class
* The date of submission

=== Introduction ===

The introduction gives the background information necessary to understand your report and should be '''2-3''' pages long (double-spaced). The introduction should be in the form of a logical argument that “funnels” from broad to narrow:

<gallery mode="nolines" widths=322px heights=256px>
Funnel.jpg
</gallery>

* States importance of the problem
* States what is known about the problem
** Give an overview of what is known about the regulation of the transcriptional response in yeast from your team's journal article and the annotated bibliography your team developed.
** Discuss how you will approach this by re-analyzing the yeast microarray data from your article.
* States what is unknown about the problem
** Which transcription factors belong in the gene regulatory network that controls the response in yeast is still unknown.
* States clues that suggest how to approach the unknown
** Introduce GRNmap and GRNsight as the answer to this problem.
* States the question the paper is trying to address
** In this case you want to discover new information about the microarray data using GRNmap and GRNsight.
* State the approach
** Creating an MS Access database to facilitate making a GRNmap input workbook that can be used by your group and other scientists in the future.

=== Combined Methods/Results/Discussion section ===

This section will summarize the entire workflow and findings for the project with contributions from all team members (Data Analyst, QA, and Coder/Designers). Create a combined flow chart of the tasks/milestones of each of the team members and then briefly describe the flow chart in the text.
* Number each of the figures sequentially and number each of the tables sequentially in order from first mention in the text. You can either embed your figures and tables in the appropriate place in the text or put them all at the end. Do not mix both styles, however.
* Write a descriptive legend for each figure and table that briefly states what the figure/table is and gives a brief key to any labels and abbreviations.

==== Data Analyst ====

* Table of ANOVA results from the [[Week 8]], discussing the interpretation of the p values.
** With an ANOVA p value < 0.05, are there more than 5% of the genes with a significant change in gene expression at any timepoint?
** Compare with what the authors of the paper considered a meaningful gene expression change.
* From the STEM analyis, include as figures the overall results (the screenshot showing all of the clusters) and then focus on the ones you interpreted for the [[Week 10]] journal assignment.
** Were there clusters shown in your journal article? How do they compare with the ones you got?
** Include a table showing the GO results for that cluster (just the narrowed down list of terms that you have interpreted).
** Discuss what the p values for the cluster and for the GO term list mean.
** Discuss the biological interpretation of your GO terms.
** Were there GO/MIPS terms in your article? Compare your terms with the article.
* Include a table that lists the transcription factors in your final network and their enrichment p value from YEASTRACT.
** Describe how and why you and your partner chose these transcription factors for your network.
** Include a figure (screenshot) of the unweighted networks visualized with GRNsight.
** What transcription factors were mentioned in your article? Are yours the same or different?
* GRNmap results
** Show the GRNsight visualization (screenshot) of the weighted networks, making sure that the genes are placed in the same relative location as each other an as the unweighted network figure.
** Provide the LSE:minLSE ratio
** Provide a table of the weights, P's, and b's
** Organize and show the individual expression plots
** Interpret the results of the model
*** What seem to be the most important transcription factors in the network? How does that compare with the journal article?

==== Coder/Designers ====

* Give a narrative description of the database, including the design of the expression tables and metadata table(s).
* Provide a database schema.

==== QA ====

* Describe the QA process for the database, noting any issues that were encountered, and stating whether data are complete or not.

=== Conclusions ===

* Write a '''1-2''' page conclusion that summarizes the overall project and your findings.
** Overall, what have you learned about the biological phenomenon described in your journal club article?
** How does the new database facilitate the data analysis?
** What future directions would you take if you were to continue this project?
** Relate the results of your project to the papers you presented for journal club and included in your bibliography in [[Week 11]].
*** Did you discover anything new that wasn't reported in the journal club paper (Data Analyst/QA)?

=== Acknowledgments ===

Write a short paragraph acknowledging the assistance of anyone who is not a member of your team.

=== References ===

* This section lists all of the references cited in the text of the report (and only those references cited in the paper). Follow the [[Media: BIOL367_Fall2019_GuidelinesforLiteratureCitations.pdf | Guidelines for Literature Citations in a Scientific Paper]] handout for general principles.
* Remember that you need to cite anything for which you are not the original source. Generally, in the introduction, you should aim for a minimum of two in-text citations per paragraph. You may reference the course web site using the appropriate format for a web reference.
* List your references in alphabetical order by first author using the [https://owl.purdue.edu/owl/research_and_citation/apa_style/apa_formatting_and_style_guide/reference_list_basic_rules.html APA style] that you have been using throughout the semester. Note that the proper format must be followed for both articles and websites, just URLs are not sufficient.

== PowerPoint Presentation ==

Each team of students will prepare and give a 20 minute PowerPoint presentation to report the results of their project on Tuesday, December 10 at 2:00-4:00 PM.
* Please follow the [http://brightspace.lmu.edu Presentation Guidelines on Brightspace] for how to format your slides.
* You will need to prepare ~20 slides (assume 1 slide per minute of presentation) and include the following content:
** Title slide that gives the main take-home message as the title of your presentation, the authors, date, and venue (course number and title).
** Outline slide that is a summary of take-home messages of your talk (should mirror your conclusion slide)
** The body of your talk (organized in a logical flow, not necessarily in the order given below).
*** Introduce the importance/significance of the problem and give background on the experiment in your journal article. You can draw from the your journal club presentation. This should follow the logic of the Introduction section of your group paper.
*** Show a combined flow chart of the tasks/milestones completed by your group (see Materials and Methods of group paper).
*** Show the table of ANOVA results.
*** Show the screenshot of the overall clustering results and the cluster you focused on.
*** Show a table of the GO results from that cluster, giving an interpretation.
*** Show the table of regulatory transcription factors in your network and their p values for enrichment.
*** Show the unweighted and weighted networks in GRNsight (with the genes arranged in the same way in each figure)
*** Database schema diagram for the MS Access database
** Conclusion slide that mirrors your outline
** Future directions
** Acknowledgments
** References
* '''''Your PowerPoint slides must be uploaded to the wiki and linked to from your individual journal page and your team page by 12:01am, Tuesday, December 10.'''''
** You can update your slides before your presentation, but we will be grading the ones you upload by the deadline.
* Your presentation (both the slides and the oral presentation) will be evaluated by the instructors using the [[Presentation Rubric]].
* Your presentation will also be evaluated by your fellow classmates (anonymously) who will answer the following questions:
*# What is the speaker's take-home message (one short sentence)?
*# What are the best points about the presentation's organization, visuals, and delivery? Please give at least 2 specific examples.
*# What points need improvement? Please give at least 2 specific examples.
* We expect that you will take the feedback from your previous presentation into account when doing this presentation.

== Individual Assessment and Reflection ==

Each person on the team will complete an assessment and reflection ''individually''. If you are comfortable with making this assessment publicly available, you may write it up as a wiki page or as a Word document uploaded to your group deliveables page. If you prefer to communicate your assessment privately, then email this to both Drs. Dahlquist and Dionisio.

=== Statement of Work ===

* Describe exactly what you did on the project.
* Provide references or links to artifacts of your work, such as:
** Wiki pages
** Other files or documents
** Code or scripts

=== Assessment of Project ===

* Give an objective assessment of the success of your project workflow and teamwork.
* What worked and what didn't work?
* What would you do differently if you could do it all over again?
* Evaluate your team’s portion of the Final Project and Group Report in the following areas:
*# Content: What is the quality of the work?
*# Organization: Comment on the organization of the project and of your group's wiki pages.
*# Completeness: Did your team achieve all of the project objectives? Why or why not?

=== Reflection on the Process ===

* What did you learn?
** With your head (biological or computer science principles)
** With your heart (personal qualities and teamwork qualities that make things work or not work)?
** With your hands (technical skills)?
* What lesson will you take away from this project that you will still use a year from now?

{{Final Project Links}}

[[Category:Group Projects]]

Final Project Deliverables

2019-11-20T18:08:13Z

Kdahlquist: /* Introduction */ add use by other scientists

{{Final Project Links}}

== Deliverables Checklist ==

# Organized Team deliverables wiki page (or other media (CD or flash drive) with table of contents)
# Group Report (''.doc'', ''.docx'' or ''.pdf'' file)
# Individual statements of work, assessments, reflections (wiki page, ''.doc'', ''.docx'', ''.pdf'', or e-mailed to Dr. Dahlquist)
# Group PowerPoint presentation (given on Tuesday, December 10, ''.ppt'', ''.pptx'' or ''.pdf'' file)
# Excel spreadsheet with ANOVA results/stem formatting (''.xlsx'')
# PowerPoint of ANOVA table, screenshots of stem results (''.pptx''), screenshot of black and white GRNsight input network and colored GRNsight output networks
# Gene List and GO List files from each significant profile (''.txt'' compressed together in a ''.zip'' file)
# YEASTRACT "rank by TF" results (''.xlsx'')
# GRNmap input workbook (with network adjacency matrix, ''.xlsx'')
# GRNmap output workbook (''.xlsx'') and output plots (''.jpg'') zipped together
# MS Access database, unified by the three teams with expression tables and metadata table(s) created (''.accdb'')
# ReadMe for the database that describes the design of the database, references the sources of the data, and has a database schema diagram (''.doc'', ''.docx'', ''.pdf'')
# Query design for populating a GRNmap input workbook from the database (screenshot of MS Access, or SQL code, ''.txt'')
# Electronic notebook corresponding to these the microarray results files ([[Week 12/13]] and [[Week 15]]) to support ''reproducible research'' so that all manipulations of the data and files are documented so that someone else could begin with your starting file, follow the protocol, and obtain your results.

=== Grading ===

* The Group Report is worth 50 points.
* The Group PowerPoint presentation is worth 50 points.
* All other deliverables listed above, together, are worth 50 points.

== Group Report ==

* The report should be written with contributions from all group members.
* Submit as ''.doc'', ''.docx'' or ''.pdf'' file.

=== Style Sheet ===

Use the following guidelines when formatting your report:
* 2.54 cm (1 in) margins on all sides
* Double-spaced
* 12 point Times/Times New Roman font
* Number the pages on the lower-right corner
* Use left justification (“jagged” on the right side)

=== Title Page ===

Include the following information in a standalone title page:
* A descriptive title for your project
** The function of the title is to identify the main result or take-home message of the paper. It should be as specific as possible. It can be a phrase or a sentence. What is the main result of your paper that you want to convey with the title?
* The names of the team members (with middle initials)
* The course number and title of the class
* The date of submission

=== Introduction ===

The introduction gives the background information necessary to understand your report and should be '''2-3''' pages long (double-spaced). The introduction should be in the form of a logical argument that “funnels” from broad to narrow:

<gallery mode="nolines" widths=322px heights=256px>
Funnel.jpg
</gallery>

* States importance of the problem
* States what is known about the problem
** Give an overview of what is known about the regulation of the transcriptional response in yeast from your team's journal article and the annotated bibliography your team developed.
** Discuss how you will approach this by re-analyzing the yeast microarray data from your article.
* States what is unknown about the problem
** Which transcription factors belong in the gene regulatory network that controls the response in yeast is still unknown.
* States clues that suggest how to approach the unknown
** Introduce GRNmap and GRNsight as the answer to this problem.
* States the question the paper is trying to address
** In this case you want to discover new information about the microarray data using GRNmap and GRNsight.
* State the approach
** Creating an MS Access database to facilitate making a GRNmap input workbook that can be used by your group and other scientists in the future.

=== Combined Methods/Results/Discussion section ===

This section will summarize the entire workflow and findings for the project with contributions from all team members (Data Analyst, QA, and Coder/Designers). Create a combined flow chart of the tasks/milestones of each of the team members and then briefly describe the flow chart in the text.
* Number each of the figures sequentially and number each of the tables sequentially in order from first mention in the text. You can either embed your figures and tables in the appropriate place in the text or put them all at the end. Do not mix both styles, however.
* Write a descriptive legend for each figure and table that briefly states what the figure/table is and gives a brief key to any labels and abbreviations.

==== Data Analyst ====

* Table of ANOVA results from the [[Week 8]], discussing the interpretation of the p values.
** With an ANOVA p value < 0.05, are there more than 5% of the genes with a significant change in gene expression at any timepoint?
** Compare with what the authors of the paper considered a meaningful gene expression change.
* From the STEM analyis, include as figures the overall results (the screenshot showing all of the clusters) and then focus on the ones you interpreted for the [[Week 10]] journal assignment.
** Were there clusters shown in your journal article? How do they compare with the ones you got?
** Include a table showing the GO results for that cluster (just the narrowed down list of terms that you have interpreted).
** Discuss what the p values for the cluster and for the GO term list mean.
** Discuss the biological interpretation of your GO terms.
** Were there GO/MIPS terms in your article? Compare your terms with the article.
* Include a table that lists the transcription factors in your final network and their enrichment p value from YEASTRACT.
** Describe how and why you and your partner chose these transcription factors for your network.
** Include a figure (screenshot) of the unweighted networks visualized with GRNsight.
** What transcription factors were mentioned in your article? Are yours the same or different?
* GRNmap results
** Show the GRNsight visualization (screenshot) of the weighted networks, making sure that the genes are placed in the same relative location as each other an as the unweighted network figure.
** Provide the LSE:minLSE ratio
** Provide a table of the weights, P's, and b's
** Organize and show the individual expression plots
** Interpret the results of the model
*** What seem to be the most important transcription factors in the network? How does that compare with the journal article?

==== Coder/Designers ====

* Give a narrative description of the database, including the design of the expression tables and metadata table(s).
* Provide a database schema.

==== QA ====

* Describe the QA process for the database, noting any issues that were encountered, and stating whether data are complete or not.

=== Conclusions ===

* Write a '''1-2''' page conclusion that summarizes the overall project and your findings.
** Overall, what have you learned about the biological phenomenon described in your journal club article?
** How does the new database facilitate the data analysis?
** What future directions would you take if you were to continue this project?
** Relate the results of your project to the papers you presented for journal club and included in your bibliography in [[Week 11]].
*** Did you discover anything new that wasn't reported in the journal club paper (Data Analyst/QA)?

=== Acknowledgments ===

Write a short paragraph acknowledging the assistance of anyone who is not a member of your team.

=== References ===

* This section lists all of the references cited in the text of the report (and only those references cited in the paper). Follow the [[Media: BIOL367_Fall2019_GuidelinesforLiteratureCitations.pdf | Guidelines for Literature Citations in a Scientific Paper]] handout for general principles.
* Remember that you need to cite anything for which you are not the original source. Generally, in the introduction, you should aim for a minimum of two in-text citations per paragraph. You may reference the course web site using the appropriate format for a web reference.
* List your references in alphabetical order by first author using the [https://owl.purdue.edu/owl/research_and_citation/apa_style/apa_formatting_and_style_guide/reference_list_basic_rules.html APA style] that you have been using throughout the semester. Note that the proper format must be followed for both articles and websites, just URLs are not sufficient.

== PowerPoint Presentation ==

Each team of students will prepare and give a 20 minute PowerPoint presentation to report the results of their project on Tuesday, December 10 at 2:00-4:00 PM.
* Please follow the [http://brightspace.lmu.edu Presentation Guidelines on Brightspace] for how to format your slides.
* You will need to prepare ~20 slides (assume 1 slide per minute of presentation) and include the following content:
** Title slide that gives the main take-home message as the title of your presentation, the authors, date, and venue (course number and title).
** Outline slide that is a summary of take-home messages of your talk (should mirror your conclusion slide)
** The body of your talk (organized in a logical flow, not necessarily in the order given below).
*** Introduce the importance/significance of the problem and give background on the experiment in your journal article. You can draw from the your journal club presentation. This should follow the logic of the Introduction section of your group paper.
*** Show a combined flow chart of the tasks/milestones completed by your group (see Materials and Methods of group paper).
*** Show the table of ANOVA results.
*** Show the screenshot of the overall clustering results and the cluster you focused on.
*** Show a table of the GO results from that cluster, giving an interpretation.
*** Show the table of regulatory transcription factors in your network and their p values for enrichment.
*** Show the unweighted and weighted networks in GRNsight (with the genes arranged in the same way in each figure)
*** Database schema diagram for the MS Access database
** Conclusion slide that mirrors your outline
** Future directions
** Acknowledgments
** References
* '''''Your PowerPoint slides must be uploaded to the wiki and linked to from your individual journal page and your team page by 12:01am, Tuesday, December 10.'''''
** You can update your slides before your presentation, but we will be grading the ones you upload by the deadline.
* Your presentation (both the slides and the oral presentation) will be evaluated by the instructors using the [[Presentation Rubric]].
* Your presentation will also be evaluated by your fellow classmates (anonymously) who will answer the following questions:
*# What is the speaker's take-home message (one short sentence)?
*# What are the best points about the presentation's organization, visuals, and delivery? Please give at least 2 specific examples.
*# What points need improvement? Please give at least 2 specific examples.
* We expect that you will take the feedback from your previous presentation into account when doing this presentation.

== Individual Assessment and Reflection ==

Each person on the team will complete an assessment and reflection ''individually''. If you are comfortable with making this assessment publicly available, you may write it up as a wiki page or as a Word document uploaded to your group deliveables page. If you prefer to communicate your assessment privately, then email this to both Drs. Dahlquist and Dionisio.

=== Statement of Work ===

* Describe exactly what you did on the project.
* Provide references or links to artifacts of your work, such as:
** Wiki pages
** Other files or documents
** Code or scripts

=== Assessment of Project ===

* Give an objective assessment of the success of your project workflow and teamwork.
* What worked and what didn't work?
* What would you do differently if you could do it all over again?
* Evaluate your team’s portion of the Final Project and Group Report in the following areas:
*# Content: What is the quality of the work?
*# Organization: Comment on the organization of the project and of your group's wiki pages.
*# Completeness: Did your team achieve all of the project objectives? Why or why not?

=== Reflection on the Process ===

* What did you learn?
** With your head (biological or computer science principles)
** With your heart (personal qualities and teamwork qualities that make things work or not work)?
** With your hands (technical skills)?
* What lesson will you take away from this project that you will still use a year from now?

{{Final Project Links}}

[[Category:Group Projects]]

Final Project Deliverables

2019-11-20T18:07:39Z

Kdahlquist: /* Data Analyst */ additional details about how to compare with the journal article

{{Final Project Links}}

== Deliverables Checklist ==

# Organized Team deliverables wiki page (or other media (CD or flash drive) with table of contents)
# Group Report (''.doc'', ''.docx'' or ''.pdf'' file)
# Individual statements of work, assessments, reflections (wiki page, ''.doc'', ''.docx'', ''.pdf'', or e-mailed to Dr. Dahlquist)
# Group PowerPoint presentation (given on Tuesday, December 10, ''.ppt'', ''.pptx'' or ''.pdf'' file)
# Excel spreadsheet with ANOVA results/stem formatting (''.xlsx'')
# PowerPoint of ANOVA table, screenshots of stem results (''.pptx''), screenshot of black and white GRNsight input network and colored GRNsight output networks
# Gene List and GO List files from each significant profile (''.txt'' compressed together in a ''.zip'' file)
# YEASTRACT "rank by TF" results (''.xlsx'')
# GRNmap input workbook (with network adjacency matrix, ''.xlsx'')
# GRNmap output workbook (''.xlsx'') and output plots (''.jpg'') zipped together
# MS Access database, unified by the three teams with expression tables and metadata table(s) created (''.accdb'')
# ReadMe for the database that describes the design of the database, references the sources of the data, and has a database schema diagram (''.doc'', ''.docx'', ''.pdf'')
# Query design for populating a GRNmap input workbook from the database (screenshot of MS Access, or SQL code, ''.txt'')
# Electronic notebook corresponding to these the microarray results files ([[Week 12/13]] and [[Week 15]]) to support ''reproducible research'' so that all manipulations of the data and files are documented so that someone else could begin with your starting file, follow the protocol, and obtain your results.

=== Grading ===

* The Group Report is worth 50 points.
* The Group PowerPoint presentation is worth 50 points.
* All other deliverables listed above, together, are worth 50 points.

== Group Report ==

* The report should be written with contributions from all group members.
* Submit as ''.doc'', ''.docx'' or ''.pdf'' file.

=== Style Sheet ===

Use the following guidelines when formatting your report:
* 2.54 cm (1 in) margins on all sides
* Double-spaced
* 12 point Times/Times New Roman font
* Number the pages on the lower-right corner
* Use left justification (“jagged” on the right side)

=== Title Page ===

Include the following information in a standalone title page:
* A descriptive title for your project
** The function of the title is to identify the main result or take-home message of the paper. It should be as specific as possible. It can be a phrase or a sentence. What is the main result of your paper that you want to convey with the title?
* The names of the team members (with middle initials)
* The course number and title of the class
* The date of submission

=== Introduction ===

The introduction gives the background information necessary to understand your report and should be '''2-3''' pages long (double-spaced). The introduction should be in the form of a logical argument that “funnels” from broad to narrow:

<gallery mode="nolines" widths=322px heights=256px>
Funnel.jpg
</gallery>

* States importance of the problem
* States what is known about the problem
** Give an overview of what is known about the regulation of the transcriptional response in yeast from your team's journal article and the annotated bibliography your team developed.
** Discuss how you will approach this by re-analyzing the yeast microarray data from your article.
* States what is unknown about the problem
** Which transcription factors belong in the gene regulatory network that controls the response in yeast is still unknown.
* States clues that suggest how to approach the unknown
** Introduce GRNmap and GRNsight as the answer to this problem.
* States the question the paper is trying to address
** In this case you want to discover new information about the microarray data using GRNmap and GRNsight.
* State the approach
** Creating an MS Access database to facilitate making a GRNmap input workbook.

=== Combined Methods/Results/Discussion section ===

This section will summarize the entire workflow and findings for the project with contributions from all team members (Data Analyst, QA, and Coder/Designers). Create a combined flow chart of the tasks/milestones of each of the team members and then briefly describe the flow chart in the text.
* Number each of the figures sequentially and number each of the tables sequentially in order from first mention in the text. You can either embed your figures and tables in the appropriate place in the text or put them all at the end. Do not mix both styles, however.
* Write a descriptive legend for each figure and table that briefly states what the figure/table is and gives a brief key to any labels and abbreviations.

==== Data Analyst ====

* Table of ANOVA results from the [[Week 8]], discussing the interpretation of the p values.
** With an ANOVA p value < 0.05, are there more than 5% of the genes with a significant change in gene expression at any timepoint?
** Compare with what the authors of the paper considered a meaningful gene expression change.
* From the STEM analyis, include as figures the overall results (the screenshot showing all of the clusters) and then focus on the ones you interpreted for the [[Week 10]] journal assignment.
** Were there clusters shown in your journal article? How do they compare with the ones you got?
** Include a table showing the GO results for that cluster (just the narrowed down list of terms that you have interpreted).
** Discuss what the p values for the cluster and for the GO term list mean.
** Discuss the biological interpretation of your GO terms.
** Were there GO/MIPS terms in your article? Compare your terms with the article.
* Include a table that lists the transcription factors in your final network and their enrichment p value from YEASTRACT.
** Describe how and why you and your partner chose these transcription factors for your network.
** Include a figure (screenshot) of the unweighted networks visualized with GRNsight.
** What transcription factors were mentioned in your article? Are yours the same or different?
* GRNmap results
** Show the GRNsight visualization (screenshot) of the weighted networks, making sure that the genes are placed in the same relative location as each other an as the unweighted network figure.
** Provide the LSE:minLSE ratio
** Provide a table of the weights, P's, and b's
** Organize and show the individual expression plots
** Interpret the results of the model
*** What seem to be the most important transcription factors in the network? How does that compare with the journal article?

==== Coder/Designers ====

* Give a narrative description of the database, including the design of the expression tables and metadata table(s).
* Provide a database schema.

==== QA ====

* Describe the QA process for the database, noting any issues that were encountered, and stating whether data are complete or not.

=== Conclusions ===

* Write a '''1-2''' page conclusion that summarizes the overall project and your findings.
** Overall, what have you learned about the biological phenomenon described in your journal club article?
** How does the new database facilitate the data analysis?
** What future directions would you take if you were to continue this project?
** Relate the results of your project to the papers you presented for journal club and included in your bibliography in [[Week 11]].
*** Did you discover anything new that wasn't reported in the journal club paper (Data Analyst/QA)?

=== Acknowledgments ===

Write a short paragraph acknowledging the assistance of anyone who is not a member of your team.

=== References ===

* This section lists all of the references cited in the text of the report (and only those references cited in the paper). Follow the [[Media: BIOL367_Fall2019_GuidelinesforLiteratureCitations.pdf | Guidelines for Literature Citations in a Scientific Paper]] handout for general principles.
* Remember that you need to cite anything for which you are not the original source. Generally, in the introduction, you should aim for a minimum of two in-text citations per paragraph. You may reference the course web site using the appropriate format for a web reference.
* List your references in alphabetical order by first author using the [https://owl.purdue.edu/owl/research_and_citation/apa_style/apa_formatting_and_style_guide/reference_list_basic_rules.html APA style] that you have been using throughout the semester. Note that the proper format must be followed for both articles and websites, just URLs are not sufficient.

== PowerPoint Presentation ==

Each team of students will prepare and give a 20 minute PowerPoint presentation to report the results of their project on Tuesday, December 10 at 2:00-4:00 PM.
* Please follow the [http://brightspace.lmu.edu Presentation Guidelines on Brightspace] for how to format your slides.
* You will need to prepare ~20 slides (assume 1 slide per minute of presentation) and include the following content:
** Title slide that gives the main take-home message as the title of your presentation, the authors, date, and venue (course number and title).
** Outline slide that is a summary of take-home messages of your talk (should mirror your conclusion slide)
** The body of your talk (organized in a logical flow, not necessarily in the order given below).
*** Introduce the importance/significance of the problem and give background on the experiment in your journal article. You can draw from the your journal club presentation. This should follow the logic of the Introduction section of your group paper.
*** Show a combined flow chart of the tasks/milestones completed by your group (see Materials and Methods of group paper).
*** Show the table of ANOVA results.
*** Show the screenshot of the overall clustering results and the cluster you focused on.
*** Show a table of the GO results from that cluster, giving an interpretation.
*** Show the table of regulatory transcription factors in your network and their p values for enrichment.
*** Show the unweighted and weighted networks in GRNsight (with the genes arranged in the same way in each figure)
*** Database schema diagram for the MS Access database
** Conclusion slide that mirrors your outline
** Future directions
** Acknowledgments
** References
* '''''Your PowerPoint slides must be uploaded to the wiki and linked to from your individual journal page and your team page by 12:01am, Tuesday, December 10.'''''
** You can update your slides before your presentation, but we will be grading the ones you upload by the deadline.
* Your presentation (both the slides and the oral presentation) will be evaluated by the instructors using the [[Presentation Rubric]].
* Your presentation will also be evaluated by your fellow classmates (anonymously) who will answer the following questions:
*# What is the speaker's take-home message (one short sentence)?
*# What are the best points about the presentation's organization, visuals, and delivery? Please give at least 2 specific examples.
*# What points need improvement? Please give at least 2 specific examples.
* We expect that you will take the feedback from your previous presentation into account when doing this presentation.

== Individual Assessment and Reflection ==

Each person on the team will complete an assessment and reflection ''individually''. If you are comfortable with making this assessment publicly available, you may write it up as a wiki page or as a Word document uploaded to your group deliveables page. If you prefer to communicate your assessment privately, then email this to both Drs. Dahlquist and Dionisio.

=== Statement of Work ===

* Describe exactly what you did on the project.
* Provide references or links to artifacts of your work, such as:
** Wiki pages
** Other files or documents
** Code or scripts

=== Assessment of Project ===

* Give an objective assessment of the success of your project workflow and teamwork.
* What worked and what didn't work?
* What would you do differently if you could do it all over again?
* Evaluate your team’s portion of the Final Project and Group Report in the following areas:
*# Content: What is the quality of the work?
*# Organization: Comment on the organization of the project and of your group's wiki pages.
*# Completeness: Did your team achieve all of the project objectives? Why or why not?

=== Reflection on the Process ===

* What did you learn?
** With your head (biological or computer science principles)
** With your heart (personal qualities and teamwork qualities that make things work or not work)?
** With your hands (technical skills)?
* What lesson will you take away from this project that you will still use a year from now?

{{Final Project Links}}

[[Category:Group Projects]]

Final Project Deliverables

2019-11-20T18:04:02Z

Kdahlquist: /* Introduction */ modify approach

{{Final Project Links}}

== Deliverables Checklist ==

# Organized Team deliverables wiki page (or other media (CD or flash drive) with table of contents)
# Group Report (''.doc'', ''.docx'' or ''.pdf'' file)
# Individual statements of work, assessments, reflections (wiki page, ''.doc'', ''.docx'', ''.pdf'', or e-mailed to Dr. Dahlquist)
# Group PowerPoint presentation (given on Tuesday, December 10, ''.ppt'', ''.pptx'' or ''.pdf'' file)
# Excel spreadsheet with ANOVA results/stem formatting (''.xlsx'')
# PowerPoint of ANOVA table, screenshots of stem results (''.pptx''), screenshot of black and white GRNsight input network and colored GRNsight output networks
# Gene List and GO List files from each significant profile (''.txt'' compressed together in a ''.zip'' file)
# YEASTRACT "rank by TF" results (''.xlsx'')
# GRNmap input workbook (with network adjacency matrix, ''.xlsx'')
# GRNmap output workbook (''.xlsx'') and output plots (''.jpg'') zipped together
# MS Access database, unified by the three teams with expression tables and metadata table(s) created (''.accdb'')
# ReadMe for the database that describes the design of the database, references the sources of the data, and has a database schema diagram (''.doc'', ''.docx'', ''.pdf'')
# Query design for populating a GRNmap input workbook from the database (screenshot of MS Access, or SQL code, ''.txt'')
# Electronic notebook corresponding to these the microarray results files ([[Week 12/13]] and [[Week 15]]) to support ''reproducible research'' so that all manipulations of the data and files are documented so that someone else could begin with your starting file, follow the protocol, and obtain your results.

=== Grading ===

* The Group Report is worth 50 points.
* The Group PowerPoint presentation is worth 50 points.
* All other deliverables listed above, together, are worth 50 points.

== Group Report ==

* The report should be written with contributions from all group members.
* Submit as ''.doc'', ''.docx'' or ''.pdf'' file.

=== Style Sheet ===

Use the following guidelines when formatting your report:
* 2.54 cm (1 in) margins on all sides
* Double-spaced
* 12 point Times/Times New Roman font
* Number the pages on the lower-right corner
* Use left justification (“jagged” on the right side)

=== Title Page ===

Include the following information in a standalone title page:
* A descriptive title for your project
** The function of the title is to identify the main result or take-home message of the paper. It should be as specific as possible. It can be a phrase or a sentence. What is the main result of your paper that you want to convey with the title?
* The names of the team members (with middle initials)
* The course number and title of the class
* The date of submission

=== Introduction ===

The introduction gives the background information necessary to understand your report and should be '''2-3''' pages long (double-spaced). The introduction should be in the form of a logical argument that “funnels” from broad to narrow:

<gallery mode="nolines" widths=322px heights=256px>
Funnel.jpg
</gallery>

* States importance of the problem
* States what is known about the problem
** Give an overview of what is known about the regulation of the transcriptional response in yeast from your team's journal article and the annotated bibliography your team developed.
** Discuss how you will approach this by re-analyzing the yeast microarray data from your article.
* States what is unknown about the problem
** Which transcription factors belong in the gene regulatory network that controls the response in yeast is still unknown.
* States clues that suggest how to approach the unknown
** Introduce GRNmap and GRNsight as the answer to this problem.
* States the question the paper is trying to address
** In this case you want to discover new information about the microarray data using GRNmap and GRNsight.
* State the approach
** Creating an MS Access database to facilitate making a GRNmap input workbook.

=== Combined Methods/Results/Discussion section ===

This section will summarize the entire workflow and findings for the project with contributions from all team members (Data Analyst, QA, and Coder/Designers). Create a combined flow chart of the tasks/milestones of each of the team members and then briefly describe the flow chart in the text.
* Number each of the figures sequentially and number each of the tables sequentially in order from first mention in the text. You can either embed your figures and tables in the appropriate place in the text or put them all at the end. Do not mix both styles, however.
* Write a descriptive legend for each figure and table that briefly states what the figure/table is and gives a brief key to any labels and abbreviations.

==== Data Analyst ====

* Table of ANOVA results from the [[Week 8]], discussing the interpretation of the p values.
* From the STEM analyis, include as figures the overall results (the screenshot showing all of the clusters) and then focus on the ones you interpreted for the [[Week 10]] journal assignment.
** Include a table showing the GO results for that cluster (just the narrowed down list of terms that you have interpreted).
** Discuss what the p values for the cluster and for the GO term list mean.
** Discuss the biological interpretation of your GO terms.
* Include a table that lists the transcription factors in your final network and their enrichment p value from YEASTRACT.
** Describe how and why you and your partner chose these transcription factors for your network.
** Include a figure (screenshot) of the unweighted networks visualized with GRNsight.
* GRNmap results
** Show the GRNsight visualization (screenshot) of the weighted networks, making sure that the genes are placed in the same relative location as each other an as the unweighted network figure.
** Provide the LSE:minLSE ratio
** Provide a table of the weights, P's, and b's
** Organize and show the individual expression plots
** Interpret the results of the model

==== Coder/Designers ====

* Give a narrative description of the database, including the design of the expression tables and metadata table(s).
* Provide a database schema.

==== QA ====

* Describe the QA process for the database, noting any issues that were encountered, and stating whether data are complete or not.

=== Conclusions ===

* Write a '''1-2''' page conclusion that summarizes the overall project and your findings.
** Overall, what have you learned about the biological phenomenon described in your journal club article?
** How does the new database facilitate the data analysis?
** What future directions would you take if you were to continue this project?
** Relate the results of your project to the papers you presented for journal club and included in your bibliography in [[Week 11]].
*** Did you discover anything new that wasn't reported in the journal club paper (Data Analyst/QA)?

=== Acknowledgments ===

Write a short paragraph acknowledging the assistance of anyone who is not a member of your team.

=== References ===

* This section lists all of the references cited in the text of the report (and only those references cited in the paper). Follow the [[Media: BIOL367_Fall2019_GuidelinesforLiteratureCitations.pdf | Guidelines for Literature Citations in a Scientific Paper]] handout for general principles.
* Remember that you need to cite anything for which you are not the original source. Generally, in the introduction, you should aim for a minimum of two in-text citations per paragraph. You may reference the course web site using the appropriate format for a web reference.
* List your references in alphabetical order by first author using the [https://owl.purdue.edu/owl/research_and_citation/apa_style/apa_formatting_and_style_guide/reference_list_basic_rules.html APA style] that you have been using throughout the semester. Note that the proper format must be followed for both articles and websites, just URLs are not sufficient.

== PowerPoint Presentation ==

Each team of students will prepare and give a 20 minute PowerPoint presentation to report the results of their project on Tuesday, December 10 at 2:00-4:00 PM.
* Please follow the [http://brightspace.lmu.edu Presentation Guidelines on Brightspace] for how to format your slides.
* You will need to prepare ~20 slides (assume 1 slide per minute of presentation) and include the following content:
** Title slide that gives the main take-home message as the title of your presentation, the authors, date, and venue (course number and title).
** Outline slide that is a summary of take-home messages of your talk (should mirror your conclusion slide)
** The body of your talk (organized in a logical flow, not necessarily in the order given below).
*** Introduce the importance/significance of the problem and give background on the experiment in your journal article. You can draw from the your journal club presentation. This should follow the logic of the Introduction section of your group paper.
*** Show a combined flow chart of the tasks/milestones completed by your group (see Materials and Methods of group paper).
*** Show the table of ANOVA results.
*** Show the screenshot of the overall clustering results and the cluster you focused on.
*** Show a table of the GO results from that cluster, giving an interpretation.
*** Show the table of regulatory transcription factors in your network and their p values for enrichment.
*** Show the unweighted and weighted networks in GRNsight (with the genes arranged in the same way in each figure)
*** Database schema diagram for the MS Access database
** Conclusion slide that mirrors your outline
** Future directions
** Acknowledgments
** References
* '''''Your PowerPoint slides must be uploaded to the wiki and linked to from your individual journal page and your team page by 12:01am, Tuesday, December 10.'''''
** You can update your slides before your presentation, but we will be grading the ones you upload by the deadline.
* Your presentation (both the slides and the oral presentation) will be evaluated by the instructors using the [[Presentation Rubric]].
* Your presentation will also be evaluated by your fellow classmates (anonymously) who will answer the following questions:
*# What is the speaker's take-home message (one short sentence)?
*# What are the best points about the presentation's organization, visuals, and delivery? Please give at least 2 specific examples.
*# What points need improvement? Please give at least 2 specific examples.
* We expect that you will take the feedback from your previous presentation into account when doing this presentation.

== Individual Assessment and Reflection ==

Each person on the team will complete an assessment and reflection ''individually''. If you are comfortable with making this assessment publicly available, you may write it up as a wiki page or as a Word document uploaded to your group deliveables page. If you prefer to communicate your assessment privately, then email this to both Drs. Dahlquist and Dionisio.

=== Statement of Work ===

* Describe exactly what you did on the project.
* Provide references or links to artifacts of your work, such as:
** Wiki pages
** Other files or documents
** Code or scripts

=== Assessment of Project ===

* Give an objective assessment of the success of your project workflow and teamwork.
* What worked and what didn't work?
* What would you do differently if you could do it all over again?
* Evaluate your team’s portion of the Final Project and Group Report in the following areas:
*# Content: What is the quality of the work?
*# Organization: Comment on the organization of the project and of your group's wiki pages.
*# Completeness: Did your team achieve all of the project objectives? Why or why not?

=== Reflection on the Process ===

* What did you learn?
** With your head (biological or computer science principles)
** With your heart (personal qualities and teamwork qualities that make things work or not work)?
** With your hands (technical skills)?
* What lesson will you take away from this project that you will still use a year from now?

{{Final Project Links}}

[[Category:Group Projects]]

Main Page

2019-11-20T18:01:52Z

Kdahlquist: /* Upcoming Seminars */ delete old seminar

__NOTOC__
<div style="text-align: center; font-size: 175%">Loyola Marymount University</div>
<div style="text-align: center; font-size: 250%; line-height: 1.25em">'''BIOL 367-01: Biological Databases'''</div>
<div style="text-align: center; font-size: 175%; margin-bottom: 0.85em">Fall 2019</div>

<div class="toc-container">__TOC__</div>

== Announcements ==

* Data Analysts and QA's who have a partner in their group can have a shared ''individual'' journal entry. Both students will be given the same grade and are expected to contribute equally to the electronic lab notebook.

=== Upcoming Seminars ===

* '''Biology Department Seminar:''' Friday, November 22, 2:00-3:00 pm, LSB Auditorium, Dr. Darcy K. Ernst, Las Positas College, ''Timing reproduction in an unpredictable world''

== Instructor ==
* [[User:Kdahlquist|Kam D. Dahlquist]]

== Students ==

{| style="margin-left: auto; margin-right: auto"
| style="padding-right: 1em" | [[User:Marmas|Michael Armas]]
| style="padding-right: 1em" | [[User:Cdomin12|Christina Dominguez]]
| style="padding-right: 1em" | [[User:knguye66|Kaitlyn Nguyen]]
| style="padding-right: 1em" | [[User:Ntesfaio|Naomi Tesfaiohannes]]
|-
| style="padding-right: 1em" | [[User:mavila9|Marcus Avila]]
| style="padding-right: 1em" | [[User:Imacarae|Ivy-Quynh Macaraeg]]
| style="padding-right: 1em" | [[User:Jnimmers| John Nimmers-Minor]]
| style="padding-right: 1em" | [[User:eyoung20|Emma Young]]
|-
| style="padding-right: 1em" | [[User:Jcowan4|Jonar Cowan]]
| style="padding-right: 1em" | [[User:Dmadere | DeLisa Madere]]
| style="padding-right: 1em" | [[User:dramir36| David Ramirez]]
| style="padding-right: 1em" |
|-
| style="padding-right: 1em" | [[User:Icrespin| Iliana Crespin]]
| style="padding-right: 1em" | [[User:ymesfin| Yeabsira Mesfin]]
| style="padding-right: 1em" | [[User:msamdars| Mihir Samdarshi]]
| style="padding-right: 1em" |
|}

{{Final Project Links}}

== Course Schedule ==

The current course schedule is shown below. In addition to the listed in-class agendas, the following guidelines are also notable:

* Unless otherwise stated on the schedule, your weekly journals/assignments are due on Thursdays at 12:01am. In cases where subsequent revisions or corrections will be accepted, the instructor will provide feedback and submission deadlines on a per-assignment basis.
* Reading assignments should be completed ''before'' coming to class.

=== Part 1: Building Blocks (Genetic Code and Manipulating Text) ===

{| class="schedule-table"
! #
! Date
! Reading
! Agenda
! Journal
|-
! 1
| Tuesday<br/>8/27/2019
|
|
* Syllabus walkthrough
* Pairwise interviews and introductions
* Academic Honesty case studies
* Class wiki account: Set up a username and password for this wiki site
| rowspan="2" | [[Week 1]]
(due at 12:01am 9/5)

[[Class_Journal_Week_1 | Class Journal Week 1]]
|-
!
| Thursday<br/>8/29/2019
| [http://brightspace.lmu.edu Chapter 1 of ''On Becoming a Biologist''] by John Janovy, Jr. (on Brightspace)

[http://dl.acm.org/ft_gateway.cfm?id=1378711 ''Voices of Computing''] and [http://dl.acm.org/ft_gateway.cfm?id=1272529 ''Computing is a Natural Science''] by Peter Denning (these links should be accessed from within LMU to get the full article)

|
* What is a model?
** Model of a cell/model of a computer

** [http://brightspace.lmu.edu Slides on Brightspace]

{{Course Schedule Divider}}
|-
! 2
| Tuesday<br/>9/3/2019
|
|
* Discuss the [[Week 1]] assignment
** Wiki syntax overview
** Lab session to setup User wiki page

| rowspan="2" | [[Week 2]]
(due at 12:01am 9/12)

[[Class Journal Week 2]]

|-
!
| Thursday<br/>9/5/2019
|
* [http://www.ncbi.nlm.nih.gov/books/NBK21134/#A5234 Ch. 1 ''Genomes 2'']
* [http://www.ncbi.nlm.nih.gov/books/NBK21121/ Brown, T.A. (2002) ''Genomes 2'', Ch. 3, especially section 3.3.2]
* On [http://brightspace.lmu.edu Brightspace]:
** Nirenberg (2004) ''Deciphering the Genetic Code''
** Kaji & Kaji (2004) ''Setting the Record Straight''
** Moody (2004) ''Digital Code of Life'', Ch. 1
** Hayes (2004) ''Ode to the Code''

|
* DNA structure
** [http://brightspace.lmu.edu Slides on Brightspace]

{{Course Schedule Divider}}
|-
! 3
| Tuesday<br/>9/10/2019
|
|
* Aipotu work session

| rowspan="2" | [[Week 3]]
(due at 12:01am 9/19)

[[Class Journal Week 3]]

|-
!
| Thursday<br/>9/12/2019
|
|
* Genes as models
* Central model of molecular biology
** [http://www.youtube.com/watch?v=u9dhO0iCLww Berkeley translation video]
** This [http://www.dnai.org/a/index.html web site] has links to animations of the replication, transcription, and translation processes.
** [http://brightspace.lmu.edu Slides on Brightspace]
* [[Media:BIOL367_Fall2019_GeneticCode-CentralModel-2.pdf | Paper and pencil exercise]]

{{Course Schedule Divider}}
|-
! 4
| Tuesday<br/>9/17/2019
|
* [http://www.bloomberg.com/graphics/2015-paul-ford-what-is-code/ Ford, Paul. “What is Code?” ''Business Week'', June 11, 2015.]
* [https://www.dataone.org/sites/all/documents/education-modules/pptx/L01_DataManagement.pptx DataONE: Data Management Slides]
|
Introduction to biological databases (part 1)
* What is bioinformatics?
* Open Science Ecosystem
* DataONE: Data Management and the life cycle of data
* Slides are on [http://brightspace.lmu.edu Brightspace]

| rowspan="2" | [[Week 4]]
(due at 12:01am 9/26)

[[Class Journal Week 4]]

|-
!
| Thursday<br/>9/19/2019
|
| Introduction to biological databases (part 2)
* [http://dondi.lmu.build/share/intro/wheres-my-stuff.pdf Where's my Stuff?]
* Slides are on [http://brightspace.lmu.edu Brightspace]
* An overview of computers, networks, files, web applications, web services, and databases
* Names, URLs, identifiers
** As applied to biology: genes, species
* “Stuff” on the worldwide web
** Accessing the web on a browser

{{Course Schedule Divider}}
|-
! 5
| Tuesday<br/>9/24/2019
|
|
[[ Week 4]] work session
| rowspan="2" | [[Week 5]]
(individual due at 12:01am 10/1)

'''''Note Tuesday Due Date'''''

[[Class Journal Week 5]]

(shared due at 12:01am 10/3)

|-
!
| Thursday<br/>9/26/2019
| [https://academic.oup.com/nar/article/47/D1/D1/5280358 The 26th annual Nucleic Acids Research database issue]

[http://www.oxfordjournals.org/nar/database/a/ The NAR Molecular Biology Database Collection]

|
Introduction to biological databases (part 2)
* Terminology, types, evaluation, four "Gold Standard" databases
* A closer look at evaluating biological databases (slides on [http://brightspace.lmu.edu Brightspace])
* [http://www.dataone.org/sites/all/documents/L02_DataSharing.pptx DataONE: Data Sharing]
* [http://brightspace.lmu.edu Presentation Guidelines PowerPoint on Brightspace]

{{Course Schedule Divider}}
|-
! 6
| Tuesday<br/>10/1/2019
|
| Database presentations part 1
# group 1
# group 2
# group 3
# group 4
| rowspan="2" | [[Week 6]]
(due at 12:01am 10/10)

[[Class Journal Week 6]]
|-
!
| Thursday<br/>10/3/2019
|
| Database presentations part 2
# group 1
# group 2
# group 3

[[Presentation Rubric]]

[http://brightspace.lmu.edu/ Presentation Guidelines on Brightspace]

|}

=== Part 2: Going Deeper (Gene Expression Data and Relational Databases) ===

{| class="schedule-table"
! #
! Date
! Reading
! Agenda
! Journal
|-
! 7
| Tuesday<br/>10/8/2019
| For more on the Human Genome Project, see:
* Moody (2004) Chapter 6 (on [http://brightspace.lmu.edu Brightspace])
* [http://www.nature.com/news/2010/100623/pdf/4651000a.pdf Science After the Sequence]
* [http://www.nature.com/news/2010/100623/full/4651000a.html Nature Special: Human Genome at 10]
* [http://www.sciencemag.org/content/vol291/issue5507/index.dtl Human Genome Project articles in ''Science'']
* [http://www.nature.com/nature/journal/v409/n6822/index.html Human Genome Project articles in ''Nature'']
|
* Human Genome Project to DNA microarrays (slides on [http://brightspace.lmu.edu Brightspace])
| rowspan="2" | [[Week 7]]
(due at 12:01am 10/17)

[[Class Journal Week 7]]

|-
!
| Thursday<br/>10/10/2019
| [http://www.ncbi.nlm.nih.gov/books/NBK26818/#A1633 Alberts ''et al''. (2002) ''Molecular Biology of the Cell'', Ch. 8: Microarrays]

[http://www.nature.com/ng/journal/v21/n1s/full/ng0199supp_33.html Brown & Botstein (1999) "Microarrays" ''Nature Genetics'']

Campbell & Heyer Chapter 4 (on [http://brightspace.lmu.edu on Brightspace])

DeRisi et al. (1997) [http://www.sciencemag.org/content/278/5338/680.full ''Science'' 278: 680-686.]
| Review [[Week 6]] assignment

{{Course Schedule Divider}}
|-
! 8
| Tuesday<br/>10/15/2019
| [http://www.dataone.org/sites/all/documents/L04_DataEntryManipulation.pptx DataONE: Data Entry and Manipulation]
| More about DNA microarray analysis
* Slides on [http://brightspace.lmu.edu Brightspace]
| rowspan="2" | [[Week 8]]
(due at 12:01am 10/24)

[[Class Journal Week 8]]
|-
!
| Thursday<br/>10/17/2019
|
| Begin DNA microarray analysis part 1
* Statistical analysis of Dahlquist Lab yeast cold shock dataset
* Test your understanding: [http://xkcd.com/882/ http://xkcd.com/882/]

{{Course Schedule Divider}}
|-
! 9
| Tuesday<br/>10/22/2019
| [https://academic.oup.com/bioinformatics/article/21/suppl_1/i159/203147 Ernst & Bar-Joseph (2006) STEM: a tool for the analysis of short time series gene expression data.]
|
Microarray Data Analysis part 2
* Clustering with stem software



| rowspan="2" | [[Week 9]]
(due at 12:01am 10/31)

[[Class Journal Week 9]]

|-
!
| Thursday<br/>10/24/2019
|
* [https://academic.oup.com/nar/article/46/D1/D348/4191333 Teixeira, et al. (2017). YEASTRACT: an upgraded database for the analysis of transcription regulatory networks in ''Saccharomyces cerevisiae'']
* [https://peerj.com/articles/cs-85/ Dahlquist et al. (2016) GRNsight: a web application and service for visualizing models of small- to medium-scale gene regulatory networks]
|
Microarray Data Analysis part 3
* Finding a candidate GRN ([http://www.yeastract.com/ YEASTRACT])

{{Course Schedule Divider}}
|-
! 10
| Tuesday<br/>10/29/2019
| [https://link.springer.com/article/10.1007/s11538-015-0092-6 Dahlquist et al. (2015) Parameter Estimation for Gene Regulatory Networks from Microarray Data: Cold Shock Response in ''Saccharomyces cerevisiae'']
|
Microarray Data Analysis part 4
* Visualizing the candidate GRN ([https://dondi.github.io/GRNsight/ GRNsight])
* Dynamical systems modeling of the gene regulatory network ([http://kdahlquist.github.io/GRNmap/ GRNmap])

| rowspan="2" | [[Week 10]]
(due at 12:01am 11/7)

[[Class Journal Week 10]]
|-
!
| Thursday<br/>10/31/2019
|
|
Microarray Data Analysis part 5
* Visualizing and interpreting the modeling results ([https://dondi.github.io/GRNsight/ GRNsight])
* Group requests due in class (collected on notecards)
|}

=== Part 3: Integrating for Research (GRNsight Gene Expression Database Project) ===

{| class="schedule-table"
! #
! Date
! Reading
! Agenda
! Journal
|-
! 11
| Tuesday<br/>11/5/2019
|
| Dynamical Systems Modeling with GRNmap
| rowspan="2" | [[Week 11]]

(due at 12:01am 11/14)

'''''Thursday Due Date'''''
|-
!
| Thursday<br/>11/7/2019
|
|
Begin GRNsight Expression Database Project
* Team assignments
* Overview of project
* Work session to create Team wiki pages

{{Course Schedule Divider}}
|-
! 12
| Tuesday<br/>11/12/2019
|
|
Literature searching and annotated bibliographies
* ''Course LibGuide:'' http://libguides.lmu.edu/BIOL367
* Select microarray papers for journal club

| rowspan="2" | [[Week 12/13]]

(due at 12:01am 11/26)

'''''Note Tuesday Due Date'''''

|-
!
| Thursday<br/>11/14/2019
|
| Journal Club Presentations

[[Presentation Rubric]]

[http://brightspace.lmu.edu Presentation Guidelines PowerPoint on Brightspace]

{{Course Schedule Divider}}
|-
! 13
| Tuesday<br/>11/19/2019
|
|
* Status report
* Work session

| rowspan="2" | [[Week 12/13]]

(due at 12:01am 11/26)<br/>
'''''Note Tuesday due date'''''
|-
!
| Thursday<br/>11/21/2019
|
|
* Status report
* Work session

|-
! 14
| Tuesday<br/>11/26/2017
|
|
* Status report
* Work session
| '''''No Week 14 assignment'''''
|-
! style="background: #ded" |
| style="background: #efe; border: none" | Thursday<br/>11/28/2019
| style="background: #efe; border: none" |
| style="background: #efe; border: none" | Thanksgiving—no class
| style="background: #efe; border: none" |
{{Course Schedule Divider}}
|-
! 15
| Tuesday<br/>12/3/2019
|
|
* Status report
* Work session
| rowspan="2" | [[Week 15]]

(due at 4:00pm 12/13)
|-
!
| Thursday<br/>12/5/2019
|
|
* Status report
* Work session
{{Course Schedule Divider}}
|-
! style="background: #edd" | F
| style="background: #fee; border: none" | Tuesday<br/>12/10/2019
| style="background: #fee; border: none" |
| style="background: #fee; border: none" | '''Final project presentations 2:00-4:00 PM'''
| style="background: #fee; border: none" |
|-
!
| Friday<br/>12/13/2019 4:00 PM
|
| '''Project deliverables due 4:00 PM'''
|
|}

== Course Information ==

=== Instructor ===

==== [[User:Kdahlquist | Kam D. Dahlquist, Ph.D.]] ====
: http://kdahlquist.github.io/DahlquistLab/index.htm
: '''Phone:''' (310) 338-7697
: '''E-mail:''' kdahlquist at lmu dot edu
: '''Office:''' Life Sciences Building 289
: '''Office Hours (Fall 2019):''' Mondays & Wednesdays 2:00-3:00 PM, Tuesdays & Thursdays 4:00-5:00 PM, and by appointment; I keep a sign-up sheet next to my office door to facilitate appointment-making.

=== Prerequisites/Recommended Background ===

To take this course, you must have upper division standing in the Seaver College of Science and Engineering. Otherwise, there are no strict prerequisites. Backgrounds in biology and computer science, as well as prior experience with web, database, or information management applications, may be helpful but not necessary.

=== Class Meetings and Attendance ===
TR 2:40–3:55pm, Seaver 120 

This is a hands-on, participatory course, thus attendance at all class meetings is required. Each student is allowed two “sick” days (automatically excused absences) during the semester. Further unexcused absences from class will result in a 5% deduction from the overall course grade for each absence. Every effort should be made to attend class on oral presentation days as the content of that day's class is dependent on student participation. Unexcused absences from an oral presentation will result in a grade of zero for the presentation. The instructor should be notified as soon as possible, electronically or by phone, of the reasons for all absences.

Note that the last day to add or drop a class without a grade of W is August 30. The withdrawal or credit/no-credit status deadline is November 1.

=== Mutual Responsibilities ===

This course is designed to foster your development as a biologist and computer scientist and to give you an authentic research experience. We will be engaged together in discovering, examining, and practicing the personal qualities, technical skills, and community standards of the scientific community. While you are ultimately responsible for your own learning, you are not alone. Our class constitutes a team where we will be learning from each other. The role of the instructor is to provide the expert coaching to support and assist you on your journey. All of the exercises, readings, assignments, and policies detailed below have been designed with this purpose in mind.

=== Classroom Conduct ===

We are all responsible for maintaining a classroom and laboratory environment that is safe and conducive to learning. As such, we will observe the following:

# As an LMU Lion, by the [http://studentaffairs.lmu.edu/wellness/lmucares/aboutlmucares/livethelionscode/ Lion’s code], you are pledged to join the discourse of the academy with honesty of voice and integrity of scholarship and to show respect for staff, professors, and other students.
# You are responsible for your own learning and for being a good class citizen.
# Class will start promptly on time.
# You are expected to come to class having done the assigned reading and preparatory work so that you are ready to participate in discussions and to perform the laboratory exercises.
# You are expected to bring the required materials to each class session.
# Cell phones and other ancillary devices must be turned off and put away out of sight. Your own laptop and/or tablet may be used to conduct the class exercises, '''''provided that you have confirmed with the instructor that you have the correct versions of software installed for the exercise'''''. If, however, the laptops/tablets are being used for other purposes and become distracting to you or others, you will be asked to put them away.
# All students are governed by [https://studentaffairs.lmu.edu/media/studentaffairs/osccr/documents/1920-LMU-community-standards.pdf LMU Community Standards publication]. Disruptive behavior which is persistent or significantly interferes with classroom activities may be subject to disciplinary action. A student may be referred to the Office of Student Judicial Affairs if his or her behavior constitutes a violation of the conduct code.

=== Course Website ===

This is the course web site and wiki, hosted by http://xmlpipedb.cs.lmu.edu/biodb/fall2019/. You will need to have a user account on this wiki to be able to edit the wiki and complete coursework. Updates to the course schedule and electronic copies of all handouts, assignments, and readings will be posted to this site. You will also use the site to keep an electronic lab notebook/journal for the course. In addition, students have been automatically enrolled in the course page on [http://brightspace.lmu.edu Brightspace]. The site will be used for materials that cannot be made public on this wiki, including grades.

=== Email Communication ===

At times we will communicate with the entire class using campus email systems, so it is essential that you regularly check your lion.lmu.edu email address or forward your lion account email to your preferred email address. Messages sent to the instructor at night or on the weekend will be answered the next school day.

=== Required Materials ===

==== Texts ====

There is no required text to purchase for the course; materials will be put on reserve at Hannon Library or will be available online on this wiki or [http://brightspace.lmu.edu Brightspace] site. Specific reading assignments are given on the course schedule and should be completed before coming to class.
* Assorted handouts, articles, and sample code will be distributed throughout the semester.
* Additional information is also available on the web; do not hesitate to look for further sources of information regarding the concepts, techniques, tools, and paradigms that we will discuss.

==== Materials (must be brought to each class meeting) ====

* 3-ring binder with all course handouts
* Pen, pencil, extra paper
* USB flash drive to store data
* Account for this wiki
* [http://www.github.com GitHub account]

=== Course Description ===

The disciplines of biology and computer science come together in bioinformatics, where computational tools are needed to manage and analyze the flood of data coming from new genomics technologies. Biological databases form a significant part of this young and exciting field. This course introduces students to both the biology and computer science expertise needed to understand, use, and develop biological databases. Biology topics include the fundamentals of genetics, molecular biology, and biochemistry needed to understand the data stored in biological databases, as well as the biotechnologies used to gather these data in a high-throughput manner. Computer science topics include what biological databases are, why they are important (and needed), and the challenges that arise in compiling them effectively. Biology and computer science lectures on topics that are relevant to biological databases are coupled with hands-on experience with a variety of software packages ranging from bioinformatics utilities to general-purpose database and software development tools. After learning how to use a biological database, students will be asked to build one of their own.

==== Course Objectives and Learning Outcomes ====

This course is built upon L. Dee Fink’s ''taxonomy of significant learning'', as applied to biological databases. Long after the course concludes, our hope is that:
* You understand how biological information is encoded in the genome and can apply this knowledge to a variety of biological tasks and problems
* You understand the core concepts, structure, and functions of a database, ranging from individual files to a full relational database management system, and can perform useful tasks with such data
* You show discipline and proficiency in day-to-day science and engineering best practices, such as maintaining journals and notebooks, managing your files and code, and critically evaluating scientific and technical information
* You recognize and care about how the biological and technological issues presented in this course relate to and affect society, our daily lives, and ourselves
* You have some skills and tools for “leaving your comfort zone,” flourishing outside of it, and learning more about biology and computer science on your own
* You learn how to communicate and work effectively with colleagues from different disciplines

==== University Core Curriculum ====

This course fulfills the following requirements in the University Core Curriculum:
* Integrations: Interdisciplinary Connections
* Upper Division Information Literacy Flag
* Upper Division Oral Communication Flag

=== Course Work and Grading ===

Your work in this course will be assessed in the following areas:

{| style="border: 1px dashed rgb(47, 111, 171); background: rgb(249, 249, 249); margin: 1em 0; padding: 1em" cellpadding="5ex"
| Weekly electronic lab notebook/journal assignments, individual (usually 10 points each)
| style="text-align: right" | 150
| points
|-
| Weekly electronic lab notebook/journal assignments, shared (usually 3 points each)
| style="text-align: right" | 50
| points
|-
| Oral presentations
| style="text-align: right" | 120
| points 
|-
| Final Project Deliverables (including written report)
| style="text-align: right" | 100
| points
|-
| '''Total'''
| style="text-align: right" | '''420'''
| '''points'''
|}

Final course grading scale:
94.0-100.0% A
90.0- 93.9% A-
86.0- 89.9% B+
82.0- 85.9% B
78.0- 81.9% B-
74.0- 77.9% C+
70.0- 73.9% C
67.0- 69.9% C-
60.0- 66.9% D
≤ 59.9% F

Current grades will be made available at [http://brightspace.lmu.edu Brightspace].

==== Electronic Laboratory Notebook/Journal ====

One of the most important skills you can develop as a scientist is keeping an excellent laboratory notebook. For computational research, the equivalent of the biology paper-based lab notebook is documentation of your “workflow.” For this course you will practice documentation skills by keeping an electronic lab notebook or journal. The technology we will use is this wiki. We will create and edit during the semester. The wiki software is the same one that runs Wikipedia and many other wiki sites, so proficiency gained here is easily transferable to other applications.

You will create an individual user page and make weekly entries that the instructor will read and grade. You will use the wiki to complete the assignments as well. The following guidelines apply:
* Your weekly journal entry is typically due every 12:01am on Thursday Pacific time; consult the schedule for specific due dates for each assignment.
* Each weekly assignment has an individual component and a shared component. You will earn 10 points per weekly submission for the individual journal entry and 3 points per submission for the shared journal entry. '''''Late journal entries will be accepted up to one week later for up to half credit.'''''
* The instructor will read and comment on how to improve your journal entries on your user talk pages.
* Depending on the type of assignment for that week, you may be given the opportunity to make improvements to previous journal entries as the semester progresses.
* For most weeks in the semester, you will be assigned a "homework partner". You will be expected to consult with your partner in order to complete the assignment. However, unless otherwise stated, each partner must submit his or her own work as the individual journal entry (direct copies of each other's work is not allowed).
* Generally, your journal entries will consist of:
** Your electronic laboratory notebook (workflow and other documentation) for hands-on exercises and projects
** Answers to any specific questions posed in the exercise
** Acknowledgments section (see [[Week 1]] assignment for details)
** References section (see [[Week 1]] assignment for details)
** Shared reflection on your learning, assigned readings, or ethics case studies.

==== Oral Presentations ====

You will give three oral presentations in this course. The first two will be in the format of a “Journal Club” presentation where students will present and lead discussion of research articles from the primary literature. The third will be a research presentation on your final project. '''''Because that day’s class content is dependent upon each student being ready to present and lead discussion, late journal club presentations will not be accepted. An unexcused absence from a journal club presentation will result in a grade of zero for the presentation.'''''

==== Final Group Project ====



In addition to the research presentation, the culmination of your final project will be the preparation of a written laboratory report in the style of a manuscript that could be submitted to a peer-reviewed journal. Specific instructions will be posted later in the semester. '''''The Final Written Report cannot be accepted any later than Friday, December 13 at 4:00pm. The Final Written Report must be completed to receive a passing grade in the course.'''''

==== Extra Credit ====

Students may accumulate up to 12.5 points toward their final grade in extra credit by attending Biology Department seminars and completing the seminar sheets. Each seminar attended is worth 2.5 points with up to 5 seminars (12.5 points) total. You must attend the entire seminar from start to finish and personally turn in your seminar sheet to a faculty member at the end of the seminar.

Certain non-Biology Department seminars may be approved in advance for extra credit at the instructor's discretion. To receive credit for these seminars, you must turn in a one-page '''''hard copy''''' of your summary of the seminar '''''in class, within one week''''' of the date of the seminar or they will not count as extra credit.

=== Work Load Expectations ===

In line with [http://www.lmu.edu/Assets/LMU+Credit+Hour+Policy_Final.pdf LMU’s Credit Hour Policy], the work load expectation for this course is that for every one hour (50 minutes) of classroom instruction, you will complete a minimum of two hours of out-of-class student work each week. This is a 3-unit course with 3 hours (150 minutes) of instruction per week. Thus the expectation is that you will complete 6 hours of work outside of class per week.

=== University Policy on Academic Honesty ===

Loyola Marymount University is a community dedicated to academic excellence. Academic honesty in scholarship and creative work stands at the center of LMU's academic life, and is essential for true learning and creation of knowledge to take place. As a university in the Jesuit and Marymount traditions, this community expects its members to act in accordance with the highest standards of honesty and ethics at all times. Violations of academic honesty undermine the fundamental educational mission of the University and cannot be tolerated.

Academic dishonesty will be treated as an extremely serious matter with severe consequences. '''''The minimum penalty for an instance of academic dishonesty in this course, even on a 1-point assignment or extra credit assignment, is a one-letter grade penalty in the course and a zero on the assignment.''''' It is never permissible to turn in any work that has not been authored by the student, such as work that has been copied from another student or copied from a source (including Internet) without properly acknowledging the source. It is the student's responsibility to make sure that your work meets the standard set forth in the “Academic Honesty Policy” (see http://academics.lmu.edu/honesty.) You are responsible for contacting the instructor before assignments are due to proactively resolve any questions you may have.

You are required to sign the [[Media:BIOL367_Fall2019_AcademicHonestyAgreement.pdf|Academic Honesty Agreement]] for this course.

==== Academic Honesty Resources ====
* [[Media:BIOL367_Fall2019_AcademicHonestyAgreement.pdf|Academic Honesty Agreement]]
* [http://academics.lmu.edu/media/lmuacademics/academichonesty/documents/Academic%20Honesty%20Policy%20FINAL%20Appendices--051116.pdf LMU Academic Honesty Policy and Procedures]
* [http://www.bio.davidson.edu/dept/plagiarism.html The Davidson College Department of Biology Statement on Plagiarism]
* [[Media:BIOL367_Fall2019_GuidelinesforLiteratureCitations.pdf | Guidelines for Literature Citations in a Scientific Paper]]
** [https://owl.purdue.edu/owl/research_and_citation/apa_style/apa_formatting_and_style_guide/reference_list_basic_rules.html APA Style for References]
* [http://dondi.lmu.build/share/misc/plagiarism.pdf Notes on plagiarism from an electrical engineering & computer science perspective]
* [http://libguides.lmu.edu/BIOL367 BIOL/CMSI 367 LibGuide]

=== Americans with Disabilities Act—Special Accommodations ===

Students with special needs who require reasonable modifications, special assistance, or accommodations in this course should promptly direct their request to the Disability Support Services (DSS) Office. Any student who currently has a documented disability (ADHD, Autism Spectrum Disorder, Learning, Physical, or Psychiatric) needing academic accommodations should contact the DSS Office (Daum Hall 2nd floor, 310-338-4216) as early in the semester as possible. All discussions will remain confidential. Please visit http://www.lmu.edu/dss for additional information. In addition, please schedule an appointment with the instructor early in the semester to discuss any accommodations for this course for which you have been approved.

=== Health and Wellness ===

There are many resources for student wellness on campus. A central website for students is: http://lionwellness.lmu.edu.

=== Emergency Preparedness ===

To report an emergency or suspicious activity, contact the LMU Department of Public Safety by phone (x222 or 310-338-2894) or at the nearest emergency call box. In the event of an evacuation, follow the evacuation signage throughout the building to the designated safe refuge area where you will receive further instruction from Public Safety or a Building Captain. For more safety information and preparedness tips, visit http://www.lmu.edu/emergency.

=== Revision Notice ===

If necessary, this syllabus and its contents are subject to revision; students are responsible for any changes or modifications announced in class. The most current version of this information resides on this page, the course web site at http://xmlpipedb.cs.lmu.edu/biodb/fall2019/.

Data Analysis

2019-11-20T00:40:20Z

Kdahlquist: /* Milestones */ add note about shared journal entries

{{Final Project Links}}

The role of the Data Analyst will be to apply the data analysis pipeline that you learned by analyzing the Dahlquist Lab microarray dataset to complete the analysis of a different published yeast timecourse microarray dataset. The Data Analysts are the end-users of the project, ultimately determining whether the work of the coder/designer and quality assurance members is useful to them.

== Guild Members ==

* [[Sulfiknights]]: Ivy, Marcus
* [[FunGals]]: Emma, Kaitlyn
* [[Skinny Genes]]: Aby, David

== Milestones ==

The milestones do not necessarily correspond to particular days/weeks; instead they are sets of tasks grouped together.

* Data Analysts and QA's who have a partner in their group can have a shared ''individual'' journal entry. Both students will be given the same grade and are expected to contribute equally to the electronic lab notebook.

=== Milestone 1: Annotated Bibliography ===

* The Data Analysts will work with their teams to develop an annotated bibliography of papers relating to their team's assigned paper.

=== Milestone 2: Journal Club Presentation ===

* The Data Analysts will work with their teams to create and deliver a Journal Club presentation about to their team's assigned paper.

=== Milestone 3: Getting the data ready for analysis ===

# Download and examine the microarray dataset, comparing it to the samples and experiment described in your journal club article.
#* [https://sgd-prod-upload.s3.amazonaws.com/S000204227/Barreto_2012_PMID_23039231.zip Barreto et al. (2012)]
#* [https://sgd-prod-upload.s3.amazonaws.com/S000204415/Kitagawa_2002_PMID_12269742.zip Kitagawa et al. (2002)]
#* [https://sgd-prod-upload.s3.amazonaws.com/S000204367/Thorsen_2007_PMID_17327492.zip Thorsen et al. (2007)]
# Along with the QA's, make a "sample-data relationship table" that lists all of the samples (microarray chips), noting the treatment, time point, and replicate number.
#* Come up with consistent column headers that summarize this information
#** For example, the Dahlquist Lab microarray data used strain_LogFC_timepoint-replicate number, as in wt_LogFC_t15-1.
# Organize the data in a worksheet in an Excel workbook so that:
#* ID is in the first column
#* Data columns are to the right, in increasing chronological order, using the column header pattern you created
#* Replicates are grouped together

=== Milestone 4: ANOVA analysis ===

# Perform an ANOVA analysis of the data, as you did on [[Week 8]] for the Dahlquist lab data.
#* Note that you will need to adjust your formulas to take into account the different number of timepoints and replicates in your article's dataset.

=== Milestone 5: Clustering with stem and YEASTRACT ===

# Cluster the data with stem, as you did on [[Week 9]].
#* Note that we will make some adjustments to the GO term analysis because stem was not providing GO term names.
# Use YEASTRACT to generate a candidate gene regulatory network as you did on [[Week 9]].

=== Milestone 6: Create an input workbook for GRNmap using MS Access database ===

# Create an input workbook for GRNmap based on a Microsoft Access database that the Coder/Designer and QA's make, following protocol in [[Week 10]]
# Run GRNmap and interpret data.
# As the end-user of the Access database, the Data Analysts will provide feedback to the QAs and Coder/Designer about the usability of database.

{{Final Project Links}}

[[Category:Group Projects]]

Quality Assurance

2019-11-20T00:40:01Z

Kdahlquist: /* Milestones */ add note about shared journals

{{Final Project Links}}

The QA team member is the link between the Coder/Designer and the Data Analyst. He or she needs to know the details of the microarray dataset being analyzed so that the database being designed and populated by the Coder/Designer is correct and useful for the Data Analyst. The QA also makes sure that the data analysis steps are being performed correctly and are being correctly documented.

== Guild Members ==

* [[FunGals]]: Iliana
* [[Skinny Genes]]: Jonar, Christina
* [[Sulfiknights]]: Naomi, Joey

== Milestones ==

The milestones do not necessarily correspond to particular weeks; instead they are sets of tasks grouped together.

* Data Analysts and QA's who have a partner in their group can have a shared ''individual'' journal entry. Both students will be given the same grade and are expected to contribute equally to the electronic lab notebook.

=== Milestone 1: Annotated Bibliography ===

* The QA's will work with their teams to develop an annotated bibliography of papers relating to their team's assigned paper.

=== Milestone 2: Journal Club Presentation ===

* The QA's will work with their teams to create and deliver a Journal Club presentation about to their team's assigned paper.

=== Milestone 3: Working with Data Analysts to understand microarray dataset ===

As an overview, the QA team member is the link between the Coder/Designer and the Data Analyst. He or she needs to know the details of the microarray dataset being analyzed so that the database being designed and populated by the Coder/Designer is correct and useful for the Data Analyst. The QA will independently check that all of the data retrieved from SGD is present and accurately represented in the MS Access database. The QA will also provide assistance to the Data Analyst, making sure that the data analysis steps are being performed correctly and are being correctly documented.

Initially, the QA's will need to do the following:
* Along with the Data Analysts, download the microarray data associated with your group's article.
** [https://sgd-prod-upload.s3.amazonaws.com/S000204227/Barreto_2012_PMID_23039231.zip Barreto et al. (2012)]
** [https://sgd-prod-upload.s3.amazonaws.com/S000204415/Kitagawa_2002_PMID_12269742.zip Kitagawa et al. (2002)]
** [https://sgd-prod-upload.s3.amazonaws.com/S000204367/Thorsen_2007_PMID_17327492.zip Thorsen et al. (2007)]
** Along with the Data Analysts, make a "sample-data relationship table" that lists all of the samples (microarray chips), noting the treatment, time point, and replicate number.
** Are all the samples described in the paper in the dataset?
** Are all the samples in the dataset described in the paper?
* Come up with consistent column headers that summarize this information
** For example, the Dahlquist Lab microarray data used strain_LogFC_timepoint-replicate number, as in wt_LogFC_t15-1.
* Organize the data in a worksheet in an Excel workbook so that:
* ID is in the first column
* Data columns are to the right, in increasing chronological order, using the column header pattern you created
* Replicates are grouped together

=== Milestone 4: Making sure expression data has both Sytematic Name and Standard Name ID's ===

* The design of the expression tables in the final database will need both an ID field (yeast systematic name) and Standard Name fields.
* You will need to check the IDs in the expression data and potentially populate one or both of these fields.
* One way to do this is use the [http://www.yeastract.com/formorftogene.php "ORF List <-> Gene List"] tool at YEASTRACT.
* The [http://llama.mshri.on.ca/synergizer/translate/ Synergizer] website may also be helpful.
* Here is a [https://rdrr.io/bioc/ClusterJudge/man/convert_Yeast_SGDId_2_systematic.html Bioconductor package] for it, too.

=== Milestone 5: Work with Coder/Designers to Design a Database to Store Time-course Microarray Data from four sources ===

* Databases created by the teams will be kept in a [https://lmu.box.com/s/gutpb5qm0a6b2pvjn1j6moqb6y47e903 "BIOL367_Fall2019 > Final Project Database" Box folder].
* Coder/Designer guild members have rights as editor to this folder; all others in the class can only view/download.
* This folder will serve as as the version control mechanism for the Coder/Designer guild.
* Designer/Coders will work with the QA's to create a MS Access Database to store the yeast time-course microarray data for the dataset being analyzed by the Data Analysts.
* The starting point will be the database already used for the [[Week 10]] assignment, which can be found [https://lmu.box.com/s/kn8l6r639af683ioojqoce5w1g3z7kd2 here] on Box.
** This database is already populated with tables for the Dahlquist Lab microarray data, degradation rates from Neymotin et al. (2014), and initial guesses for production rates.
*** You may need to change the table names of these existing tables so that they make sense with the overall database design.
** You will need to add one or more expression tables for the expression data from your team's article.
*** Work with your team's QA and Data Analysts to determine appropriate column headings for the expression table.
** You will also need to create one or more tables with metadata about the other tables because now the database will contain data from multiple sources, not just one.
*** A major part of the design work will be to figure out what information needs to be in the metadata table so that queries can be easily and uniquely performed on the data.

=== Milestone 6: Validation and Quality Assurance on Database ===

* After the Access database is built by the Coder/Designer, the QA will perform quality assurance to make sure that the database is correct and accurate.
** In particular, the QA needs to make sure that all of the rows of data were imported into the database for the expression table(s).
** The QA will make sure that both the ID (SGD systematic name) and Standard Names are included in each expression table and are correct.
* QA's will communicate to the Coder/Designers any changes needed to the database.

{{Final Project Links}}

[[Category:Group Projects]]

Main Page

2019-11-20T00:39:36Z

Kdahlquist: /* Announcements */ add announcement

__NOTOC__
<div style="text-align: center; font-size: 175%">Loyola Marymount University</div>
<div style="text-align: center; font-size: 250%; line-height: 1.25em">'''BIOL 367-01: Biological Databases'''</div>
<div style="text-align: center; font-size: 175%; margin-bottom: 0.85em">Fall 2019</div>

<div class="toc-container">__TOC__</div>

== Announcements ==

* Data Analysts and QA's who have a partner in their group can have a shared ''individual'' journal entry. Both students will be given the same grade and are expected to contribute equally to the electronic lab notebook.

=== Upcoming Seminars ===

* '''Environmental Lecture Series:''' Tuesday, November 19, 4:30-5:30 pm, McIntosh Center (UH 3999), Dr. Rachel Adams, LMU Professor of Civil Engineering and Director of Environmental Science, ''Assessing Legacy Contaminants on a Remote Northwestern Hawaiian Island''
* '''Biology Department Seminar:''' Friday, November 22, 2:00-3:00 pm, LSB Auditorium, Dr. Darcy K. Ernst, Las Positas College, ''Timing reproduction in an unpredictable world''

== Instructor ==
* [[User:Kdahlquist|Kam D. Dahlquist]]

== Students ==

{| style="margin-left: auto; margin-right: auto"
| style="padding-right: 1em" | [[User:Marmas|Michael Armas]]
| style="padding-right: 1em" | [[User:Cdomin12|Christina Dominguez]]
| style="padding-right: 1em" | [[User:knguye66|Kaitlyn Nguyen]]
| style="padding-right: 1em" | [[User:Ntesfaio|Naomi Tesfaiohannes]]
|-
| style="padding-right: 1em" | [[User:mavila9|Marcus Avila]]
| style="padding-right: 1em" | [[User:Imacarae|Ivy-Quynh Macaraeg]]
| style="padding-right: 1em" | [[User:Jnimmers| John Nimmers-Minor]]
| style="padding-right: 1em" | [[User:eyoung20|Emma Young]]
|-
| style="padding-right: 1em" | [[User:Jcowan4|Jonar Cowan]]
| style="padding-right: 1em" | [[User:Dmadere | DeLisa Madere]]
| style="padding-right: 1em" | [[User:dramir36| David Ramirez]]
| style="padding-right: 1em" |
|-
| style="padding-right: 1em" | [[User:Icrespin| Iliana Crespin]]
| style="padding-right: 1em" | [[User:ymesfin| Yeabsira Mesfin]]
| style="padding-right: 1em" | [[User:msamdars| Mihir Samdarshi]]
| style="padding-right: 1em" |
|}

{{Final Project Links}}

== Course Schedule ==

The current course schedule is shown below. In addition to the listed in-class agendas, the following guidelines are also notable:

* Unless otherwise stated on the schedule, your weekly journals/assignments are due on Thursdays at 12:01am. In cases where subsequent revisions or corrections will be accepted, the instructor will provide feedback and submission deadlines on a per-assignment basis.
* Reading assignments should be completed ''before'' coming to class.

=== Part 1: Building Blocks (Genetic Code and Manipulating Text) ===

{| class="schedule-table"
! #
! Date
! Reading
! Agenda
! Journal
|-
! 1
| Tuesday<br/>8/27/2019
|
|
* Syllabus walkthrough
* Pairwise interviews and introductions
* Academic Honesty case studies
* Class wiki account: Set up a username and password for this wiki site
| rowspan="2" | [[Week 1]]
(due at 12:01am 9/5)

[[Class_Journal_Week_1 | Class Journal Week 1]]
|-
!
| Thursday<br/>8/29/2019
| [http://brightspace.lmu.edu Chapter 1 of ''On Becoming a Biologist''] by John Janovy, Jr. (on Brightspace)

[http://dl.acm.org/ft_gateway.cfm?id=1378711 ''Voices of Computing''] and [http://dl.acm.org/ft_gateway.cfm?id=1272529 ''Computing is a Natural Science''] by Peter Denning (these links should be accessed from within LMU to get the full article)

|
* What is a model?
** Model of a cell/model of a computer

** [http://brightspace.lmu.edu Slides on Brightspace]

{{Course Schedule Divider}}
|-
! 2
| Tuesday<br/>9/3/2019
|
|
* Discuss the [[Week 1]] assignment
** Wiki syntax overview
** Lab session to setup User wiki page

| rowspan="2" | [[Week 2]]
(due at 12:01am 9/12)

[[Class Journal Week 2]]

|-
!
| Thursday<br/>9/5/2019
|
* [http://www.ncbi.nlm.nih.gov/books/NBK21134/#A5234 Ch. 1 ''Genomes 2'']
* [http://www.ncbi.nlm.nih.gov/books/NBK21121/ Brown, T.A. (2002) ''Genomes 2'', Ch. 3, especially section 3.3.2]
* On [http://brightspace.lmu.edu Brightspace]:
** Nirenberg (2004) ''Deciphering the Genetic Code''
** Kaji & Kaji (2004) ''Setting the Record Straight''
** Moody (2004) ''Digital Code of Life'', Ch. 1
** Hayes (2004) ''Ode to the Code''

|
* DNA structure
** [http://brightspace.lmu.edu Slides on Brightspace]

{{Course Schedule Divider}}
|-
! 3
| Tuesday<br/>9/10/2019
|
|
* Aipotu work session

| rowspan="2" | [[Week 3]]
(due at 12:01am 9/19)

[[Class Journal Week 3]]

|-
!
| Thursday<br/>9/12/2019
|
|
* Genes as models
* Central model of molecular biology
** [http://www.youtube.com/watch?v=u9dhO0iCLww Berkeley translation video]
** This [http://www.dnai.org/a/index.html web site] has links to animations of the replication, transcription, and translation processes.
** [http://brightspace.lmu.edu Slides on Brightspace]
* [[Media:BIOL367_Fall2019_GeneticCode-CentralModel-2.pdf | Paper and pencil exercise]]

{{Course Schedule Divider}}
|-
! 4
| Tuesday<br/>9/17/2019
|
* [http://www.bloomberg.com/graphics/2015-paul-ford-what-is-code/ Ford, Paul. “What is Code?” ''Business Week'', June 11, 2015.]
* [https://www.dataone.org/sites/all/documents/education-modules/pptx/L01_DataManagement.pptx DataONE: Data Management Slides]
|
Introduction to biological databases (part 1)
* What is bioinformatics?
* Open Science Ecosystem
* DataONE: Data Management and the life cycle of data
* Slides are on [http://brightspace.lmu.edu Brightspace]

| rowspan="2" | [[Week 4]]
(due at 12:01am 9/26)

[[Class Journal Week 4]]

|-
!
| Thursday<br/>9/19/2019
|
| Introduction to biological databases (part 2)
* [http://dondi.lmu.build/share/intro/wheres-my-stuff.pdf Where's my Stuff?]
* Slides are on [http://brightspace.lmu.edu Brightspace]
* An overview of computers, networks, files, web applications, web services, and databases
* Names, URLs, identifiers
** As applied to biology: genes, species
* “Stuff” on the worldwide web
** Accessing the web on a browser

{{Course Schedule Divider}}
|-
! 5
| Tuesday<br/>9/24/2019
|
|
[[ Week 4]] work session
| rowspan="2" | [[Week 5]]
(individual due at 12:01am 10/1)

'''''Note Tuesday Due Date'''''

[[Class Journal Week 5]]

(shared due at 12:01am 10/3)

|-
!
| Thursday<br/>9/26/2019
| [https://academic.oup.com/nar/article/47/D1/D1/5280358 The 26th annual Nucleic Acids Research database issue]

[http://www.oxfordjournals.org/nar/database/a/ The NAR Molecular Biology Database Collection]

|
Introduction to biological databases (part 2)
* Terminology, types, evaluation, four "Gold Standard" databases
* A closer look at evaluating biological databases (slides on [http://brightspace.lmu.edu Brightspace])
* [http://www.dataone.org/sites/all/documents/L02_DataSharing.pptx DataONE: Data Sharing]
* [http://brightspace.lmu.edu Presentation Guidelines PowerPoint on Brightspace]

{{Course Schedule Divider}}
|-
! 6
| Tuesday<br/>10/1/2019
|
| Database presentations part 1
# group 1
# group 2
# group 3
# group 4
| rowspan="2" | [[Week 6]]
(due at 12:01am 10/10)

[[Class Journal Week 6]]
|-
!
| Thursday<br/>10/3/2019
|
| Database presentations part 2
# group 1
# group 2
# group 3

[[Presentation Rubric]]

[http://brightspace.lmu.edu/ Presentation Guidelines on Brightspace]

|}

=== Part 2: Going Deeper (Gene Expression Data and Relational Databases) ===

{| class="schedule-table"
! #
! Date
! Reading
! Agenda
! Journal
|-
! 7
| Tuesday<br/>10/8/2019
| For more on the Human Genome Project, see:
* Moody (2004) Chapter 6 (on [http://brightspace.lmu.edu Brightspace])
* [http://www.nature.com/news/2010/100623/pdf/4651000a.pdf Science After the Sequence]
* [http://www.nature.com/news/2010/100623/full/4651000a.html Nature Special: Human Genome at 10]
* [http://www.sciencemag.org/content/vol291/issue5507/index.dtl Human Genome Project articles in ''Science'']
* [http://www.nature.com/nature/journal/v409/n6822/index.html Human Genome Project articles in ''Nature'']
|
* Human Genome Project to DNA microarrays (slides on [http://brightspace.lmu.edu Brightspace])
| rowspan="2" | [[Week 7]]
(due at 12:01am 10/17)

[[Class Journal Week 7]]

|-
!
| Thursday<br/>10/10/2019
| [http://www.ncbi.nlm.nih.gov/books/NBK26818/#A1633 Alberts ''et al''. (2002) ''Molecular Biology of the Cell'', Ch. 8: Microarrays]

[http://www.nature.com/ng/journal/v21/n1s/full/ng0199supp_33.html Brown & Botstein (1999) "Microarrays" ''Nature Genetics'']

Campbell & Heyer Chapter 4 (on [http://brightspace.lmu.edu on Brightspace])

DeRisi et al. (1997) [http://www.sciencemag.org/content/278/5338/680.full ''Science'' 278: 680-686.]
| Review [[Week 6]] assignment

{{Course Schedule Divider}}
|-
! 8
| Tuesday<br/>10/15/2019
| [http://www.dataone.org/sites/all/documents/L04_DataEntryManipulation.pptx DataONE: Data Entry and Manipulation]
| More about DNA microarray analysis
* Slides on [http://brightspace.lmu.edu Brightspace]
| rowspan="2" | [[Week 8]]
(due at 12:01am 10/24)

[[Class Journal Week 8]]
|-
!
| Thursday<br/>10/17/2019
|
| Begin DNA microarray analysis part 1
* Statistical analysis of Dahlquist Lab yeast cold shock dataset
* Test your understanding: [http://xkcd.com/882/ http://xkcd.com/882/]

{{Course Schedule Divider}}
|-
! 9
| Tuesday<br/>10/22/2019
| [https://academic.oup.com/bioinformatics/article/21/suppl_1/i159/203147 Ernst & Bar-Joseph (2006) STEM: a tool for the analysis of short time series gene expression data.]
|
Microarray Data Analysis part 2
* Clustering with stem software



| rowspan="2" | [[Week 9]]
(due at 12:01am 10/31)

[[Class Journal Week 9]]

|-
!
| Thursday<br/>10/24/2019
|
* [https://academic.oup.com/nar/article/46/D1/D348/4191333 Teixeira, et al. (2017). YEASTRACT: an upgraded database for the analysis of transcription regulatory networks in ''Saccharomyces cerevisiae'']
* [https://peerj.com/articles/cs-85/ Dahlquist et al. (2016) GRNsight: a web application and service for visualizing models of small- to medium-scale gene regulatory networks]
|
Microarray Data Analysis part 3
* Finding a candidate GRN ([http://www.yeastract.com/ YEASTRACT])

{{Course Schedule Divider}}
|-
! 10
| Tuesday<br/>10/29/2019
| [https://link.springer.com/article/10.1007/s11538-015-0092-6 Dahlquist et al. (2015) Parameter Estimation for Gene Regulatory Networks from Microarray Data: Cold Shock Response in ''Saccharomyces cerevisiae'']
|
Microarray Data Analysis part 4
* Visualizing the candidate GRN ([https://dondi.github.io/GRNsight/ GRNsight])
* Dynamical systems modeling of the gene regulatory network ([http://kdahlquist.github.io/GRNmap/ GRNmap])

| rowspan="2" | [[Week 10]]
(due at 12:01am 11/7)

[[Class Journal Week 10]]
|-
!
| Thursday<br/>10/31/2019
|
|
Microarray Data Analysis part 5
* Visualizing and interpreting the modeling results ([https://dondi.github.io/GRNsight/ GRNsight])
* Group requests due in class (collected on notecards)
|}

=== Part 3: Integrating for Research (GRNsight Gene Expression Database Project) ===

{| class="schedule-table"
! #
! Date
! Reading
! Agenda
! Journal
|-
! 11
| Tuesday<br/>11/5/2019
|
| Dynamical Systems Modeling with GRNmap
| rowspan="2" | [[Week 11]]

(due at 12:01am 11/14)

'''''Thursday Due Date'''''
|-
!
| Thursday<br/>11/7/2019
|
|
Begin GRNsight Expression Database Project
* Team assignments
* Overview of project
* Work session to create Team wiki pages

{{Course Schedule Divider}}
|-
! 12
| Tuesday<br/>11/12/2019
|
|
Literature searching and annotated bibliographies
* ''Course LibGuide:'' http://libguides.lmu.edu/BIOL367
* Select microarray papers for journal club

| rowspan="2" | [[Week 12/13]]

(due at 12:01am 11/26)

'''''Note Tuesday Due Date'''''

|-
!
| Thursday<br/>11/14/2019
|
| Journal Club Presentations

[[Presentation Rubric]]

[http://brightspace.lmu.edu Presentation Guidelines PowerPoint on Brightspace]

{{Course Schedule Divider}}
|-
! 13
| Tuesday<br/>11/19/2019
|
|
* Status report
* Work session

| rowspan="2" | [[Week 12/13]]

(due at 12:01am 11/26)<br/>
'''''Note Tuesday due date'''''
|-
!
| Thursday<br/>11/21/2019
|
|
* Status report
* Work session

|-
! 14
| Tuesday<br/>11/26/2017
|
|
* Status report
* Work session
| '''''No Week 14 assignment'''''
|-
! style="background: #ded" |
| style="background: #efe; border: none" | Thursday<br/>11/28/2019
| style="background: #efe; border: none" |
| style="background: #efe; border: none" | Thanksgiving—no class
| style="background: #efe; border: none" |
{{Course Schedule Divider}}
|-
! 15
| Tuesday<br/>12/3/2019
|
|
* Status report
* Work session
| rowspan="2" | [[Week 15]]

(due at 4:00pm 12/13)
|-
!
| Thursday<br/>12/5/2019
|
|
* Status report
* Work session
{{Course Schedule Divider}}
|-
! style="background: #edd" | F
| style="background: #fee; border: none" | Tuesday<br/>12/10/2019
| style="background: #fee; border: none" |
| style="background: #fee; border: none" | '''Final project presentations 2:00-4:00 PM'''
| style="background: #fee; border: none" |
|-
!
| Friday<br/>12/13/2019 4:00 PM
|
| '''Project deliverables due 4:00 PM'''
|
|}

== Course Information ==

=== Instructor ===

==== [[User:Kdahlquist | Kam D. Dahlquist, Ph.D.]] ====
: http://kdahlquist.github.io/DahlquistLab/index.htm
: '''Phone:''' (310) 338-7697
: '''E-mail:''' kdahlquist at lmu dot edu
: '''Office:''' Life Sciences Building 289
: '''Office Hours (Fall 2019):''' Mondays & Wednesdays 2:00-3:00 PM, Tuesdays & Thursdays 4:00-5:00 PM, and by appointment; I keep a sign-up sheet next to my office door to facilitate appointment-making.

=== Prerequisites/Recommended Background ===

To take this course, you must have upper division standing in the Seaver College of Science and Engineering. Otherwise, there are no strict prerequisites. Backgrounds in biology and computer science, as well as prior experience with web, database, or information management applications, may be helpful but not necessary.

=== Class Meetings and Attendance ===
TR 2:40–3:55pm, Seaver 120 

This is a hands-on, participatory course, thus attendance at all class meetings is required. Each student is allowed two “sick” days (automatically excused absences) during the semester. Further unexcused absences from class will result in a 5% deduction from the overall course grade for each absence. Every effort should be made to attend class on oral presentation days as the content of that day's class is dependent on student participation. Unexcused absences from an oral presentation will result in a grade of zero for the presentation. The instructor should be notified as soon as possible, electronically or by phone, of the reasons for all absences.

Note that the last day to add or drop a class without a grade of W is August 30. The withdrawal or credit/no-credit status deadline is November 1.

=== Mutual Responsibilities ===

This course is designed to foster your development as a biologist and computer scientist and to give you an authentic research experience. We will be engaged together in discovering, examining, and practicing the personal qualities, technical skills, and community standards of the scientific community. While you are ultimately responsible for your own learning, you are not alone. Our class constitutes a team where we will be learning from each other. The role of the instructor is to provide the expert coaching to support and assist you on your journey. All of the exercises, readings, assignments, and policies detailed below have been designed with this purpose in mind.

=== Classroom Conduct ===

We are all responsible for maintaining a classroom and laboratory environment that is safe and conducive to learning. As such, we will observe the following:

# As an LMU Lion, by the [http://studentaffairs.lmu.edu/wellness/lmucares/aboutlmucares/livethelionscode/ Lion’s code], you are pledged to join the discourse of the academy with honesty of voice and integrity of scholarship and to show respect for staff, professors, and other students.
# You are responsible for your own learning and for being a good class citizen.
# Class will start promptly on time.
# You are expected to come to class having done the assigned reading and preparatory work so that you are ready to participate in discussions and to perform the laboratory exercises.
# You are expected to bring the required materials to each class session.
# Cell phones and other ancillary devices must be turned off and put away out of sight. Your own laptop and/or tablet may be used to conduct the class exercises, '''''provided that you have confirmed with the instructor that you have the correct versions of software installed for the exercise'''''. If, however, the laptops/tablets are being used for other purposes and become distracting to you or others, you will be asked to put them away.
# All students are governed by [https://studentaffairs.lmu.edu/media/studentaffairs/osccr/documents/1920-LMU-community-standards.pdf LMU Community Standards publication]. Disruptive behavior which is persistent or significantly interferes with classroom activities may be subject to disciplinary action. A student may be referred to the Office of Student Judicial Affairs if his or her behavior constitutes a violation of the conduct code.

=== Course Website ===

This is the course web site and wiki, hosted by http://xmlpipedb.cs.lmu.edu/biodb/fall2019/. You will need to have a user account on this wiki to be able to edit the wiki and complete coursework. Updates to the course schedule and electronic copies of all handouts, assignments, and readings will be posted to this site. You will also use the site to keep an electronic lab notebook/journal for the course. In addition, students have been automatically enrolled in the course page on [http://brightspace.lmu.edu Brightspace]. The site will be used for materials that cannot be made public on this wiki, including grades.

=== Email Communication ===

At times we will communicate with the entire class using campus email systems, so it is essential that you regularly check your lion.lmu.edu email address or forward your lion account email to your preferred email address. Messages sent to the instructor at night or on the weekend will be answered the next school day.

=== Required Materials ===

==== Texts ====

There is no required text to purchase for the course; materials will be put on reserve at Hannon Library or will be available online on this wiki or [http://brightspace.lmu.edu Brightspace] site. Specific reading assignments are given on the course schedule and should be completed before coming to class.
* Assorted handouts, articles, and sample code will be distributed throughout the semester.
* Additional information is also available on the web; do not hesitate to look for further sources of information regarding the concepts, techniques, tools, and paradigms that we will discuss.

==== Materials (must be brought to each class meeting) ====

* 3-ring binder with all course handouts
* Pen, pencil, extra paper
* USB flash drive to store data
* Account for this wiki
* [http://www.github.com GitHub account]

=== Course Description ===

The disciplines of biology and computer science come together in bioinformatics, where computational tools are needed to manage and analyze the flood of data coming from new genomics technologies. Biological databases form a significant part of this young and exciting field. This course introduces students to both the biology and computer science expertise needed to understand, use, and develop biological databases. Biology topics include the fundamentals of genetics, molecular biology, and biochemistry needed to understand the data stored in biological databases, as well as the biotechnologies used to gather these data in a high-throughput manner. Computer science topics include what biological databases are, why they are important (and needed), and the challenges that arise in compiling them effectively. Biology and computer science lectures on topics that are relevant to biological databases are coupled with hands-on experience with a variety of software packages ranging from bioinformatics utilities to general-purpose database and software development tools. After learning how to use a biological database, students will be asked to build one of their own.

==== Course Objectives and Learning Outcomes ====

This course is built upon L. Dee Fink’s ''taxonomy of significant learning'', as applied to biological databases. Long after the course concludes, our hope is that:
* You understand how biological information is encoded in the genome and can apply this knowledge to a variety of biological tasks and problems
* You understand the core concepts, structure, and functions of a database, ranging from individual files to a full relational database management system, and can perform useful tasks with such data
* You show discipline and proficiency in day-to-day science and engineering best practices, such as maintaining journals and notebooks, managing your files and code, and critically evaluating scientific and technical information
* You recognize and care about how the biological and technological issues presented in this course relate to and affect society, our daily lives, and ourselves
* You have some skills and tools for “leaving your comfort zone,” flourishing outside of it, and learning more about biology and computer science on your own
* You learn how to communicate and work effectively with colleagues from different disciplines

==== University Core Curriculum ====

This course fulfills the following requirements in the University Core Curriculum:
* Integrations: Interdisciplinary Connections
* Upper Division Information Literacy Flag
* Upper Division Oral Communication Flag

=== Course Work and Grading ===

Your work in this course will be assessed in the following areas:

{| style="border: 1px dashed rgb(47, 111, 171); background: rgb(249, 249, 249); margin: 1em 0; padding: 1em" cellpadding="5ex"
| Weekly electronic lab notebook/journal assignments, individual (usually 10 points each)
| style="text-align: right" | 150
| points
|-
| Weekly electronic lab notebook/journal assignments, shared (usually 3 points each)
| style="text-align: right" | 50
| points
|-
| Oral presentations
| style="text-align: right" | 120
| points 
|-
| Final Project Deliverables (including written report)
| style="text-align: right" | 100
| points
|-
| '''Total'''
| style="text-align: right" | '''420'''
| '''points'''
|}

Final course grading scale:
94.0-100.0% A
90.0- 93.9% A-
86.0- 89.9% B+
82.0- 85.9% B
78.0- 81.9% B-
74.0- 77.9% C+
70.0- 73.9% C
67.0- 69.9% C-
60.0- 66.9% D
≤ 59.9% F

Current grades will be made available at [http://brightspace.lmu.edu Brightspace].

==== Electronic Laboratory Notebook/Journal ====

One of the most important skills you can develop as a scientist is keeping an excellent laboratory notebook. For computational research, the equivalent of the biology paper-based lab notebook is documentation of your “workflow.” For this course you will practice documentation skills by keeping an electronic lab notebook or journal. The technology we will use is this wiki. We will create and edit during the semester. The wiki software is the same one that runs Wikipedia and many other wiki sites, so proficiency gained here is easily transferable to other applications.

You will create an individual user page and make weekly entries that the instructor will read and grade. You will use the wiki to complete the assignments as well. The following guidelines apply:
* Your weekly journal entry is typically due every 12:01am on Thursday Pacific time; consult the schedule for specific due dates for each assignment.
* Each weekly assignment has an individual component and a shared component. You will earn 10 points per weekly submission for the individual journal entry and 3 points per submission for the shared journal entry. '''''Late journal entries will be accepted up to one week later for up to half credit.'''''
* The instructor will read and comment on how to improve your journal entries on your user talk pages.
* Depending on the type of assignment for that week, you may be given the opportunity to make improvements to previous journal entries as the semester progresses.
* For most weeks in the semester, you will be assigned a "homework partner". You will be expected to consult with your partner in order to complete the assignment. However, unless otherwise stated, each partner must submit his or her own work as the individual journal entry (direct copies of each other's work is not allowed).
* Generally, your journal entries will consist of:
** Your electronic laboratory notebook (workflow and other documentation) for hands-on exercises and projects
** Answers to any specific questions posed in the exercise
** Acknowledgments section (see [[Week 1]] assignment for details)
** References section (see [[Week 1]] assignment for details)
** Shared reflection on your learning, assigned readings, or ethics case studies.

==== Oral Presentations ====

You will give three oral presentations in this course. The first two will be in the format of a “Journal Club” presentation where students will present and lead discussion of research articles from the primary literature. The third will be a research presentation on your final project. '''''Because that day’s class content is dependent upon each student being ready to present and lead discussion, late journal club presentations will not be accepted. An unexcused absence from a journal club presentation will result in a grade of zero for the presentation.'''''

==== Final Group Project ====



In addition to the research presentation, the culmination of your final project will be the preparation of a written laboratory report in the style of a manuscript that could be submitted to a peer-reviewed journal. Specific instructions will be posted later in the semester. '''''The Final Written Report cannot be accepted any later than Friday, December 13 at 4:00pm. The Final Written Report must be completed to receive a passing grade in the course.'''''

==== Extra Credit ====

Students may accumulate up to 12.5 points toward their final grade in extra credit by attending Biology Department seminars and completing the seminar sheets. Each seminar attended is worth 2.5 points with up to 5 seminars (12.5 points) total. You must attend the entire seminar from start to finish and personally turn in your seminar sheet to a faculty member at the end of the seminar.

Certain non-Biology Department seminars may be approved in advance for extra credit at the instructor's discretion. To receive credit for these seminars, you must turn in a one-page '''''hard copy''''' of your summary of the seminar '''''in class, within one week''''' of the date of the seminar or they will not count as extra credit.

=== Work Load Expectations ===

In line with [http://www.lmu.edu/Assets/LMU+Credit+Hour+Policy_Final.pdf LMU’s Credit Hour Policy], the work load expectation for this course is that for every one hour (50 minutes) of classroom instruction, you will complete a minimum of two hours of out-of-class student work each week. This is a 3-unit course with 3 hours (150 minutes) of instruction per week. Thus the expectation is that you will complete 6 hours of work outside of class per week.

=== University Policy on Academic Honesty ===

Loyola Marymount University is a community dedicated to academic excellence. Academic honesty in scholarship and creative work stands at the center of LMU's academic life, and is essential for true learning and creation of knowledge to take place. As a university in the Jesuit and Marymount traditions, this community expects its members to act in accordance with the highest standards of honesty and ethics at all times. Violations of academic honesty undermine the fundamental educational mission of the University and cannot be tolerated.

Academic dishonesty will be treated as an extremely serious matter with severe consequences. '''''The minimum penalty for an instance of academic dishonesty in this course, even on a 1-point assignment or extra credit assignment, is a one-letter grade penalty in the course and a zero on the assignment.''''' It is never permissible to turn in any work that has not been authored by the student, such as work that has been copied from another student or copied from a source (including Internet) without properly acknowledging the source. It is the student's responsibility to make sure that your work meets the standard set forth in the “Academic Honesty Policy” (see http://academics.lmu.edu/honesty.) You are responsible for contacting the instructor before assignments are due to proactively resolve any questions you may have.

You are required to sign the [[Media:BIOL367_Fall2019_AcademicHonestyAgreement.pdf|Academic Honesty Agreement]] for this course.

==== Academic Honesty Resources ====
* [[Media:BIOL367_Fall2019_AcademicHonestyAgreement.pdf|Academic Honesty Agreement]]
* [http://academics.lmu.edu/media/lmuacademics/academichonesty/documents/Academic%20Honesty%20Policy%20FINAL%20Appendices--051116.pdf LMU Academic Honesty Policy and Procedures]
* [http://www.bio.davidson.edu/dept/plagiarism.html The Davidson College Department of Biology Statement on Plagiarism]
* [[Media:BIOL367_Fall2019_GuidelinesforLiteratureCitations.pdf | Guidelines for Literature Citations in a Scientific Paper]]
** [https://owl.purdue.edu/owl/research_and_citation/apa_style/apa_formatting_and_style_guide/reference_list_basic_rules.html APA Style for References]
* [http://dondi.lmu.build/share/misc/plagiarism.pdf Notes on plagiarism from an electrical engineering & computer science perspective]
* [http://libguides.lmu.edu/BIOL367 BIOL/CMSI 367 LibGuide]

=== Americans with Disabilities Act—Special Accommodations ===

Students with special needs who require reasonable modifications, special assistance, or accommodations in this course should promptly direct their request to the Disability Support Services (DSS) Office. Any student who currently has a documented disability (ADHD, Autism Spectrum Disorder, Learning, Physical, or Psychiatric) needing academic accommodations should contact the DSS Office (Daum Hall 2nd floor, 310-338-4216) as early in the semester as possible. All discussions will remain confidential. Please visit http://www.lmu.edu/dss for additional information. In addition, please schedule an appointment with the instructor early in the semester to discuss any accommodations for this course for which you have been approved.

=== Health and Wellness ===

There are many resources for student wellness on campus. A central website for students is: http://lionwellness.lmu.edu.

=== Emergency Preparedness ===

To report an emergency or suspicious activity, contact the LMU Department of Public Safety by phone (x222 or 310-338-2894) or at the nearest emergency call box. In the event of an evacuation, follow the evacuation signage throughout the building to the designated safe refuge area where you will receive further instruction from Public Safety or a Building Captain. For more safety information and preparedness tips, visit http://www.lmu.edu/emergency.

=== Revision Notice ===

If necessary, this syllabus and its contents are subject to revision; students are responsible for any changes or modifications announced in class. The most current version of this information resides on this page, the course web site at http://xmlpipedb.cs.lmu.edu/biodb/fall2019/.

Quality Assurance

2019-11-19T23:52:59Z

Kdahlquist: /* Milestone 4: Making sure expression data has both Sytematic Name and Standard Name ID's */ add link to R

{{Final Project Links}}

The QA team member is the link between the Coder/Designer and the Data Analyst. He or she needs to know the details of the microarray dataset being analyzed so that the database being designed and populated by the Coder/Designer is correct and useful for the Data Analyst. The QA also makes sure that the data analysis steps are being performed correctly and are being correctly documented.

== Guild Members ==

* [[FunGals]]: Iliana
* [[Skinny Genes]]: Jonar, Christina
* [[Sulfiknights]]: Naomi, Joey

== Milestones ==

The milestones do not necessarily correspond to particular weeks; instead they are sets of tasks grouped together.

=== Milestone 1: Annotated Bibliography ===

* The QA's will work with their teams to develop an annotated bibliography of papers relating to their team's assigned paper.

=== Milestone 2: Journal Club Presentation ===

* The QA's will work with their teams to create and deliver a Journal Club presentation about to their team's assigned paper.

=== Milestone 3: Working with Data Analysts to understand microarray dataset ===

As an overview, the QA team member is the link between the Coder/Designer and the Data Analyst. He or she needs to know the details of the microarray dataset being analyzed so that the database being designed and populated by the Coder/Designer is correct and useful for the Data Analyst. The QA will independently check that all of the data retrieved from SGD is present and accurately represented in the MS Access database. The QA will also provide assistance to the Data Analyst, making sure that the data analysis steps are being performed correctly and are being correctly documented.

Initially, the QA's will need to do the following:
* Along with the Data Analysts, download the microarray data associated with your group's article.
** [https://sgd-prod-upload.s3.amazonaws.com/S000204227/Barreto_2012_PMID_23039231.zip Barreto et al. (2012)]
** [https://sgd-prod-upload.s3.amazonaws.com/S000204415/Kitagawa_2002_PMID_12269742.zip Kitagawa et al. (2002)]
** [https://sgd-prod-upload.s3.amazonaws.com/S000204367/Thorsen_2007_PMID_17327492.zip Thorsen et al. (2007)]
** Along with the Data Analysts, make a "sample-data relationship table" that lists all of the samples (microarray chips), noting the treatment, time point, and replicate number.
** Are all the samples described in the paper in the dataset?
** Are all the samples in the dataset described in the paper?
* Come up with consistent column headers that summarize this information
** For example, the Dahlquist Lab microarray data used strain_LogFC_timepoint-replicate number, as in wt_LogFC_t15-1.
* Organize the data in a worksheet in an Excel workbook so that:
* ID is in the first column
* Data columns are to the right, in increasing chronological order, using the column header pattern you created
* Replicates are grouped together

=== Milestone 4: Making sure expression data has both Sytematic Name and Standard Name ID's ===

* The design of the expression tables in the final database will need both an ID field (yeast systematic name) and Standard Name fields.
* You will need to check the IDs in the expression data and potentially populate one or both of these fields.
* One way to do this is use the [http://www.yeastract.com/formorftogene.php "ORF List <-> Gene List"] tool at YEASTRACT.
* The [http://llama.mshri.on.ca/synergizer/translate/ Synergizer] website may also be helpful.
* Here is a [https://rdrr.io/bioc/ClusterJudge/man/convert_Yeast_SGDId_2_systematic.html Bioconductor package] for it, too.

=== Milestone 5: Work with Coder/Designers to Design a Database to Store Time-course Microarray Data from four sources ===

* Databases created by the teams will be kept in a [https://lmu.box.com/s/gutpb5qm0a6b2pvjn1j6moqb6y47e903 "BIOL367_Fall2019 > Final Project Database" Box folder].
* Coder/Designer guild members have rights as editor to this folder; all others in the class can only view/download.
* This folder will serve as as the version control mechanism for the Coder/Designer guild.
* Designer/Coders will work with the QA's to create a MS Access Database to store the yeast time-course microarray data for the dataset being analyzed by the Data Analysts.
* The starting point will be the database already used for the [[Week 10]] assignment, which can be found [https://lmu.box.com/s/kn8l6r639af683ioojqoce5w1g3z7kd2 here] on Box.
** This database is already populated with tables for the Dahlquist Lab microarray data, degradation rates from Neymotin et al. (2014), and initial guesses for production rates.
*** You may need to change the table names of these existing tables so that they make sense with the overall database design.
** You will need to add one or more expression tables for the expression data from your team's article.
*** Work with your team's QA and Data Analysts to determine appropriate column headings for the expression table.
** You will also need to create one or more tables with metadata about the other tables because now the database will contain data from multiple sources, not just one.
*** A major part of the design work will be to figure out what information needs to be in the metadata table so that queries can be easily and uniquely performed on the data.

=== Milestone 6: Validation and Quality Assurance on Database ===

* After the Access database is built by the Coder/Designer, the QA will perform quality assurance to make sure that the database is correct and accurate.
** In particular, the QA needs to make sure that all of the rows of data were imported into the database for the expression table(s).
** The QA will make sure that both the ID (SGD systematic name) and Standard Names are included in each expression table and are correct.
* QA's will communicate to the Coder/Designers any changes needed to the database.

{{Final Project Links}}

[[Category:Group Projects]]

Quality Assurance

2019-11-19T23:44:06Z

Kdahlquist: /* Milestone 4: Making sure expression data has both Sytematic Name and Standard Name ID's */ add link to synergizer website

{{Final Project Links}}

The QA team member is the link between the Coder/Designer and the Data Analyst. He or she needs to know the details of the microarray dataset being analyzed so that the database being designed and populated by the Coder/Designer is correct and useful for the Data Analyst. The QA also makes sure that the data analysis steps are being performed correctly and are being correctly documented.

== Guild Members ==

* [[FunGals]]: Iliana
* [[Skinny Genes]]: Jonar, Christina
* [[Sulfiknights]]: Naomi, Joey

== Milestones ==

The milestones do not necessarily correspond to particular weeks; instead they are sets of tasks grouped together.

=== Milestone 1: Annotated Bibliography ===

* The QA's will work with their teams to develop an annotated bibliography of papers relating to their team's assigned paper.

=== Milestone 2: Journal Club Presentation ===

* The QA's will work with their teams to create and deliver a Journal Club presentation about to their team's assigned paper.

=== Milestone 3: Working with Data Analysts to understand microarray dataset ===

As an overview, the QA team member is the link between the Coder/Designer and the Data Analyst. He or she needs to know the details of the microarray dataset being analyzed so that the database being designed and populated by the Coder/Designer is correct and useful for the Data Analyst. The QA will independently check that all of the data retrieved from SGD is present and accurately represented in the MS Access database. The QA will also provide assistance to the Data Analyst, making sure that the data analysis steps are being performed correctly and are being correctly documented.

Initially, the QA's will need to do the following:
* Along with the Data Analysts, download the microarray data associated with your group's article.
** [https://sgd-prod-upload.s3.amazonaws.com/S000204227/Barreto_2012_PMID_23039231.zip Barreto et al. (2012)]
** [https://sgd-prod-upload.s3.amazonaws.com/S000204415/Kitagawa_2002_PMID_12269742.zip Kitagawa et al. (2002)]
** [https://sgd-prod-upload.s3.amazonaws.com/S000204367/Thorsen_2007_PMID_17327492.zip Thorsen et al. (2007)]
** Along with the Data Analysts, make a "sample-data relationship table" that lists all of the samples (microarray chips), noting the treatment, time point, and replicate number.
** Are all the samples described in the paper in the dataset?
** Are all the samples in the dataset described in the paper?
* Come up with consistent column headers that summarize this information
** For example, the Dahlquist Lab microarray data used strain_LogFC_timepoint-replicate number, as in wt_LogFC_t15-1.
* Organize the data in a worksheet in an Excel workbook so that:
* ID is in the first column
* Data columns are to the right, in increasing chronological order, using the column header pattern you created
* Replicates are grouped together

=== Milestone 4: Making sure expression data has both Sytematic Name and Standard Name ID's ===

* The design of the expression tables in the final database will need both an ID field (yeast systematic name) and Standard Name fields.
* You will need to check the IDs in the expression data and potentially populate one or both of these fields.
* One way to do this is use the [http://www.yeastract.com/formorftogene.php "ORF List <-> Gene List"] tool at YEASTRACT.
* The [http://llama.mshri.on.ca/synergizer/translate/ Synergizer] website may also be helpful.

=== Milestone 5: Work with Coder/Designers to Design a Database to Store Time-course Microarray Data from four sources ===

* Databases created by the teams will be kept in a [https://lmu.box.com/s/gutpb5qm0a6b2pvjn1j6moqb6y47e903 "BIOL367_Fall2019 > Final Project Database" Box folder].
* Coder/Designer guild members have rights as editor to this folder; all others in the class can only view/download.
* This folder will serve as as the version control mechanism for the Coder/Designer guild.
* Designer/Coders will work with the QA's to create a MS Access Database to store the yeast time-course microarray data for the dataset being analyzed by the Data Analysts.
* The starting point will be the database already used for the [[Week 10]] assignment, which can be found [https://lmu.box.com/s/kn8l6r639af683ioojqoce5w1g3z7kd2 here] on Box.
** This database is already populated with tables for the Dahlquist Lab microarray data, degradation rates from Neymotin et al. (2014), and initial guesses for production rates.
*** You may need to change the table names of these existing tables so that they make sense with the overall database design.
** You will need to add one or more expression tables for the expression data from your team's article.
*** Work with your team's QA and Data Analysts to determine appropriate column headings for the expression table.
** You will also need to create one or more tables with metadata about the other tables because now the database will contain data from multiple sources, not just one.
*** A major part of the design work will be to figure out what information needs to be in the metadata table so that queries can be easily and uniquely performed on the data.

=== Milestone 6: Validation and Quality Assurance on Database ===

* After the Access database is built by the Coder/Designer, the QA will perform quality assurance to make sure that the database is correct and accurate.
** In particular, the QA needs to make sure that all of the rows of data were imported into the database for the expression table(s).
** The QA will make sure that both the ID (SGD systematic name) and Standard Names are included in each expression table and are correct.
* QA's will communicate to the Coder/Designers any changes needed to the database.

{{Final Project Links}}

[[Category:Group Projects]]

Quality Assurance

2019-11-19T23:39:32Z

Kdahlquist: /* Milestone 3: Working with Data Analysts to understand microarray dataset */ add to sample-data-relationships

{{Final Project Links}}

The QA team member is the link between the Coder/Designer and the Data Analyst. He or she needs to know the details of the microarray dataset being analyzed so that the database being designed and populated by the Coder/Designer is correct and useful for the Data Analyst. The QA also makes sure that the data analysis steps are being performed correctly and are being correctly documented.

== Guild Members ==

* [[FunGals]]: Iliana
* [[Skinny Genes]]: Jonar, Christina
* [[Sulfiknights]]: Naomi, Joey

== Milestones ==

The milestones do not necessarily correspond to particular weeks; instead they are sets of tasks grouped together.

=== Milestone 1: Annotated Bibliography ===

* The QA's will work with their teams to develop an annotated bibliography of papers relating to their team's assigned paper.

=== Milestone 2: Journal Club Presentation ===

* The QA's will work with their teams to create and deliver a Journal Club presentation about to their team's assigned paper.

=== Milestone 3: Working with Data Analysts to understand microarray dataset ===

As an overview, the QA team member is the link between the Coder/Designer and the Data Analyst. He or she needs to know the details of the microarray dataset being analyzed so that the database being designed and populated by the Coder/Designer is correct and useful for the Data Analyst. The QA will independently check that all of the data retrieved from SGD is present and accurately represented in the MS Access database. The QA will also provide assistance to the Data Analyst, making sure that the data analysis steps are being performed correctly and are being correctly documented.

Initially, the QA's will need to do the following:
* Along with the Data Analysts, download the microarray data associated with your group's article.
** [https://sgd-prod-upload.s3.amazonaws.com/S000204227/Barreto_2012_PMID_23039231.zip Barreto et al. (2012)]
** [https://sgd-prod-upload.s3.amazonaws.com/S000204415/Kitagawa_2002_PMID_12269742.zip Kitagawa et al. (2002)]
** [https://sgd-prod-upload.s3.amazonaws.com/S000204367/Thorsen_2007_PMID_17327492.zip Thorsen et al. (2007)]
** Along with the Data Analysts, make a "sample-data relationship table" that lists all of the samples (microarray chips), noting the treatment, time point, and replicate number.
** Are all the samples described in the paper in the dataset?
** Are all the samples in the dataset described in the paper?
* Come up with consistent column headers that summarize this information
** For example, the Dahlquist Lab microarray data used strain_LogFC_timepoint-replicate number, as in wt_LogFC_t15-1.
* Organize the data in a worksheet in an Excel workbook so that:
* ID is in the first column
* Data columns are to the right, in increasing chronological order, using the column header pattern you created
* Replicates are grouped together

=== Milestone 4: Making sure expression data has both Sytematic Name and Standard Name ID's ===

* The design of the expression tables in the final database will need both an ID field (yeast systematic name) and Standard Name fields.
* You will need to check the IDs in the expression data and potentially populate one or both of these fields.
* One way to do this is use the [http://www.yeastract.com/formorftogene.php "ORF List <-> Gene List"] tool at YEASTRACT.

=== Milestone 5: Work with Coder/Designers to Design a Database to Store Time-course Microarray Data from four sources ===

* Databases created by the teams will be kept in a [https://lmu.box.com/s/gutpb5qm0a6b2pvjn1j6moqb6y47e903 "BIOL367_Fall2019 > Final Project Database" Box folder].
* Coder/Designer guild members have rights as editor to this folder; all others in the class can only view/download.
* This folder will serve as as the version control mechanism for the Coder/Designer guild.
* Designer/Coders will work with the QA's to create a MS Access Database to store the yeast time-course microarray data for the dataset being analyzed by the Data Analysts.
* The starting point will be the database already used for the [[Week 10]] assignment, which can be found [https://lmu.box.com/s/kn8l6r639af683ioojqoce5w1g3z7kd2 here] on Box.
** This database is already populated with tables for the Dahlquist Lab microarray data, degradation rates from Neymotin et al. (2014), and initial guesses for production rates.
*** You may need to change the table names of these existing tables so that they make sense with the overall database design.
** You will need to add one or more expression tables for the expression data from your team's article.
*** Work with your team's QA and Data Analysts to determine appropriate column headings for the expression table.
** You will also need to create one or more tables with metadata about the other tables because now the database will contain data from multiple sources, not just one.
*** A major part of the design work will be to figure out what information needs to be in the metadata table so that queries can be easily and uniquely performed on the data.

=== Milestone 6: Validation and Quality Assurance on Database ===

* After the Access database is built by the Coder/Designer, the QA will perform quality assurance to make sure that the database is correct and accurate.
** In particular, the QA needs to make sure that all of the rows of data were imported into the database for the expression table(s).
** The QA will make sure that both the ID (SGD systematic name) and Standard Names are included in each expression table and are correct.
* QA's will communicate to the Coder/Designers any changes needed to the database.

{{Final Project Links}}

[[Category:Group Projects]]

Final Project Deliverables

2019-11-19T23:16:27Z

Kdahlquist: /* Assessment of Project */ remove reference to gene page

{{Final Project Links}}

== Deliverables Checklist ==

# Organized Team deliverables wiki page (or other media (CD or flash drive) with table of contents)
# Group Report (''.doc'', ''.docx'' or ''.pdf'' file)
# Individual statements of work, assessments, reflections (wiki page, ''.doc'', ''.docx'', ''.pdf'', or e-mailed to Dr. Dahlquist)
# Group PowerPoint presentation (given on Tuesday, December 10, ''.ppt'', ''.pptx'' or ''.pdf'' file)
# Excel spreadsheet with ANOVA results/stem formatting (''.xlsx'')
# PowerPoint of ANOVA table, screenshots of stem results (''.pptx''), screenshot of black and white GRNsight input network and colored GRNsight output networks
# Gene List and GO List files from each significant profile (''.txt'' compressed together in a ''.zip'' file)
# YEASTRACT "rank by TF" results (''.xlsx'')
# GRNmap input workbook (with network adjacency matrix, ''.xlsx'')
# GRNmap output workbook (''.xlsx'') and output plots (''.jpg'') zipped together
# MS Access database, unified by the three teams with expression tables and metadata table(s) created (''.accdb'')
# ReadMe for the database that describes the design of the database, references the sources of the data, and has a database schema diagram (''.doc'', ''.docx'', ''.pdf'')
# Query design for populating a GRNmap input workbook from the database (screenshot of MS Access, or SQL code, ''.txt'')
# Electronic notebook corresponding to these the microarray results files ([[Week 12/13]] and [[Week 15]]) to support ''reproducible research'' so that all manipulations of the data and files are documented so that someone else could begin with your starting file, follow the protocol, and obtain your results.

=== Grading ===

* The Group Report is worth 50 points.
* The Group PowerPoint presentation is worth 50 points.
* All other deliverables listed above, together, are worth 50 points.

== Group Report ==

* The report should be written with contributions from all group members.
* Submit as ''.doc'', ''.docx'' or ''.pdf'' file.

=== Style Sheet ===

Use the following guidelines when formatting your report:
* 2.54 cm (1 in) margins on all sides
* Double-spaced
* 12 point Times/Times New Roman font
* Number the pages on the lower-right corner
* Use left justification (“jagged” on the right side)

=== Title Page ===

Include the following information in a standalone title page:
* A descriptive title for your project
** The function of the title is to identify the main result or take-home message of the paper. It should be as specific as possible. It can be a phrase or a sentence. What is the main result of your paper that you want to convey with the title?
* The names of the team members (with middle initials)
* The course number and title of the class
* The date of submission

=== Introduction ===

The introduction gives the background information necessary to understand your report and should be '''2-3''' pages long (double-spaced). The introduction should be in the form of a logical argument that “funnels” from broad to narrow:

<gallery mode="nolines" widths=322px heights=256px>
Funnel.jpg
</gallery>

* States importance of the problem
* States what is known about the problem
** Give an overview of what is known about the regulation of the transcriptional response in yeast from your team's journal article and the annotated bibliography your team developed.
** Discuss how you will approach this by re-analyzing the yeast microarray data from your article.
* States what is unknown about the problem
** Which transcription factors belong in the gene regulatory network that controls the response in yeast is still unknown.
* States clues that suggest how to approach the unknown
** Introduce GRNmap and GRNsight as the answer to this problem.
* States the question the paper is trying to address
** In this case you want to discover new information about the microarray data using GRNmap and GRNsight.
* State the approach
** Give an overview of your team's contribution to GRNsight and why it is needed.

=== Combined Methods/Results/Discussion section ===

This section will summarize the entire workflow and findings for the project with contributions from all team members (Data Analyst, QA, and Coder/Designers). Create a combined flow chart of the tasks/milestones of each of the team members and then briefly describe the flow chart in the text.
* Number each of the figures sequentially and number each of the tables sequentially in order from first mention in the text. You can either embed your figures and tables in the appropriate place in the text or put them all at the end. Do not mix both styles, however.
* Write a descriptive legend for each figure and table that briefly states what the figure/table is and gives a brief key to any labels and abbreviations.

==== Data Analyst ====

* Table of ANOVA results from the [[Week 8]], discussing the interpretation of the p values.
* From the STEM analyis, include as figures the overall results (the screenshot showing all of the clusters) and then focus on the ones you interpreted for the [[Week 10]] journal assignment.
** Include a table showing the GO results for that cluster (just the narrowed down list of terms that you have interpreted).
** Discuss what the p values for the cluster and for the GO term list mean.
** Discuss the biological interpretation of your GO terms.
* Include a table that lists the transcription factors in your final network and their enrichment p value from YEASTRACT.
** Describe how and why you and your partner chose these transcription factors for your network.
** Include a figure (screenshot) of the unweighted networks visualized with GRNsight.
* GRNmap results
** Show the GRNsight visualization (screenshot) of the weighted networks, making sure that the genes are placed in the same relative location as each other an as the unweighted network figure.
** Provide the LSE:minLSE ratio
** Provide a table of the weights, P's, and b's
** Organize and show the individual expression plots
** Interpret the results of the model

==== Coder/Designers ====

* Give a narrative description of the database, including the design of the expression tables and metadata table(s).
* Provide a database schema.

==== QA ====

* Describe the QA process for the database, noting any issues that were encountered, and stating whether data are complete or not.

=== Conclusions ===

* Write a '''1-2''' page conclusion that summarizes the overall project and your findings.
** Overall, what have you learned about the biological phenomenon described in your journal club article?
** How does the new database facilitate the data analysis?
** What future directions would you take if you were to continue this project?
** Relate the results of your project to the papers you presented for journal club and included in your bibliography in [[Week 11]].
*** Did you discover anything new that wasn't reported in the journal club paper (Data Analyst/QA)?

=== Acknowledgments ===

Write a short paragraph acknowledging the assistance of anyone who is not a member of your team.

=== References ===

* This section lists all of the references cited in the text of the report (and only those references cited in the paper). Follow the [[Media: BIOL367_Fall2019_GuidelinesforLiteratureCitations.pdf | Guidelines for Literature Citations in a Scientific Paper]] handout for general principles.
* Remember that you need to cite anything for which you are not the original source. Generally, in the introduction, you should aim for a minimum of two in-text citations per paragraph. You may reference the course web site using the appropriate format for a web reference.
* List your references in alphabetical order by first author using the [https://owl.purdue.edu/owl/research_and_citation/apa_style/apa_formatting_and_style_guide/reference_list_basic_rules.html APA style] that you have been using throughout the semester. Note that the proper format must be followed for both articles and websites, just URLs are not sufficient.

== PowerPoint Presentation ==

Each team of students will prepare and give a 20 minute PowerPoint presentation to report the results of their project on Tuesday, December 10 at 2:00-4:00 PM.
* Please follow the [http://brightspace.lmu.edu Presentation Guidelines on Brightspace] for how to format your slides.
* You will need to prepare ~20 slides (assume 1 slide per minute of presentation) and include the following content:
** Title slide that gives the main take-home message as the title of your presentation, the authors, date, and venue (course number and title).
** Outline slide that is a summary of take-home messages of your talk (should mirror your conclusion slide)
** The body of your talk (organized in a logical flow, not necessarily in the order given below).
*** Introduce the importance/significance of the problem and give background on the experiment in your journal article. You can draw from the your journal club presentation. This should follow the logic of the Introduction section of your group paper.
*** Show a combined flow chart of the tasks/milestones completed by your group (see Materials and Methods of group paper).
*** Show the table of ANOVA results.
*** Show the screenshot of the overall clustering results and the cluster you focused on.
*** Show a table of the GO results from that cluster, giving an interpretation.
*** Show the table of regulatory transcription factors in your network and their p values for enrichment.
*** Show the unweighted and weighted networks in GRNsight (with the genes arranged in the same way in each figure)
*** Database schema diagram for the MS Access database
** Conclusion slide that mirrors your outline
** Future directions
** Acknowledgments
** References
* '''''Your PowerPoint slides must be uploaded to the wiki and linked to from your individual journal page and your team page by 12:01am, Tuesday, December 10.'''''
** You can update your slides before your presentation, but we will be grading the ones you upload by the deadline.
* Your presentation (both the slides and the oral presentation) will be evaluated by the instructors using the [[Presentation Rubric]].
* Your presentation will also be evaluated by your fellow classmates (anonymously) who will answer the following questions:
*# What is the speaker's take-home message (one short sentence)?
*# What are the best points about the presentation's organization, visuals, and delivery? Please give at least 2 specific examples.
*# What points need improvement? Please give at least 2 specific examples.
* We expect that you will take the feedback from your previous presentation into account when doing this presentation.

== Individual Assessment and Reflection ==

Each person on the team will complete an assessment and reflection ''individually''. If you are comfortable with making this assessment publicly available, you may write it up as a wiki page or as a Word document uploaded to your group deliveables page. If you prefer to communicate your assessment privately, then email this to both Drs. Dahlquist and Dionisio.

=== Statement of Work ===

* Describe exactly what you did on the project.
* Provide references or links to artifacts of your work, such as:
** Wiki pages
** Other files or documents
** Code or scripts

=== Assessment of Project ===

* Give an objective assessment of the success of your project workflow and teamwork.
* What worked and what didn't work?
* What would you do differently if you could do it all over again?
* Evaluate your team’s portion of the Final Project and Group Report in the following areas:
*# Content: What is the quality of the work?
*# Organization: Comment on the organization of the project and of your group's wiki pages.
*# Completeness: Did your team achieve all of the project objectives? Why or why not?

=== Reflection on the Process ===

* What did you learn?
** With your head (biological or computer science principles)
** With your heart (personal qualities and teamwork qualities that make things work or not work)?
** With your hands (technical skills)?
* What lesson will you take away from this project that you will still use a year from now?

{{Final Project Links}}

[[Category:Group Projects]]

Final Project Deliverables

2019-11-19T23:15:56Z

Kdahlquist: /* PowerPoint Presentation */ clarified whose jc presentation

{{Final Project Links}}

== Deliverables Checklist ==

# Organized Team deliverables wiki page (or other media (CD or flash drive) with table of contents)
# Group Report (''.doc'', ''.docx'' or ''.pdf'' file)
# Individual statements of work, assessments, reflections (wiki page, ''.doc'', ''.docx'', ''.pdf'', or e-mailed to Dr. Dahlquist)
# Group PowerPoint presentation (given on Tuesday, December 10, ''.ppt'', ''.pptx'' or ''.pdf'' file)
# Excel spreadsheet with ANOVA results/stem formatting (''.xlsx'')
# PowerPoint of ANOVA table, screenshots of stem results (''.pptx''), screenshot of black and white GRNsight input network and colored GRNsight output networks
# Gene List and GO List files from each significant profile (''.txt'' compressed together in a ''.zip'' file)
# YEASTRACT "rank by TF" results (''.xlsx'')
# GRNmap input workbook (with network adjacency matrix, ''.xlsx'')
# GRNmap output workbook (''.xlsx'') and output plots (''.jpg'') zipped together
# MS Access database, unified by the three teams with expression tables and metadata table(s) created (''.accdb'')
# ReadMe for the database that describes the design of the database, references the sources of the data, and has a database schema diagram (''.doc'', ''.docx'', ''.pdf'')
# Query design for populating a GRNmap input workbook from the database (screenshot of MS Access, or SQL code, ''.txt'')
# Electronic notebook corresponding to these the microarray results files ([[Week 12/13]] and [[Week 15]]) to support ''reproducible research'' so that all manipulations of the data and files are documented so that someone else could begin with your starting file, follow the protocol, and obtain your results.

=== Grading ===

* The Group Report is worth 50 points.
* The Group PowerPoint presentation is worth 50 points.
* All other deliverables listed above, together, are worth 50 points.

== Group Report ==

* The report should be written with contributions from all group members.
* Submit as ''.doc'', ''.docx'' or ''.pdf'' file.

=== Style Sheet ===

Use the following guidelines when formatting your report:
* 2.54 cm (1 in) margins on all sides
* Double-spaced
* 12 point Times/Times New Roman font
* Number the pages on the lower-right corner
* Use left justification (“jagged” on the right side)

=== Title Page ===

Include the following information in a standalone title page:
* A descriptive title for your project
** The function of the title is to identify the main result or take-home message of the paper. It should be as specific as possible. It can be a phrase or a sentence. What is the main result of your paper that you want to convey with the title?
* The names of the team members (with middle initials)
* The course number and title of the class
* The date of submission

=== Introduction ===

The introduction gives the background information necessary to understand your report and should be '''2-3''' pages long (double-spaced). The introduction should be in the form of a logical argument that “funnels” from broad to narrow:

<gallery mode="nolines" widths=322px heights=256px>
Funnel.jpg
</gallery>

* States importance of the problem
* States what is known about the problem
** Give an overview of what is known about the regulation of the transcriptional response in yeast from your team's journal article and the annotated bibliography your team developed.
** Discuss how you will approach this by re-analyzing the yeast microarray data from your article.
* States what is unknown about the problem
** Which transcription factors belong in the gene regulatory network that controls the response in yeast is still unknown.
* States clues that suggest how to approach the unknown
** Introduce GRNmap and GRNsight as the answer to this problem.
* States the question the paper is trying to address
** In this case you want to discover new information about the microarray data using GRNmap and GRNsight.
* State the approach
** Give an overview of your team's contribution to GRNsight and why it is needed.

=== Combined Methods/Results/Discussion section ===

This section will summarize the entire workflow and findings for the project with contributions from all team members (Data Analyst, QA, and Coder/Designers). Create a combined flow chart of the tasks/milestones of each of the team members and then briefly describe the flow chart in the text.
* Number each of the figures sequentially and number each of the tables sequentially in order from first mention in the text. You can either embed your figures and tables in the appropriate place in the text or put them all at the end. Do not mix both styles, however.
* Write a descriptive legend for each figure and table that briefly states what the figure/table is and gives a brief key to any labels and abbreviations.

==== Data Analyst ====

* Table of ANOVA results from the [[Week 8]], discussing the interpretation of the p values.
* From the STEM analyis, include as figures the overall results (the screenshot showing all of the clusters) and then focus on the ones you interpreted for the [[Week 10]] journal assignment.
** Include a table showing the GO results for that cluster (just the narrowed down list of terms that you have interpreted).
** Discuss what the p values for the cluster and for the GO term list mean.
** Discuss the biological interpretation of your GO terms.
* Include a table that lists the transcription factors in your final network and their enrichment p value from YEASTRACT.
** Describe how and why you and your partner chose these transcription factors for your network.
** Include a figure (screenshot) of the unweighted networks visualized with GRNsight.
* GRNmap results
** Show the GRNsight visualization (screenshot) of the weighted networks, making sure that the genes are placed in the same relative location as each other an as the unweighted network figure.
** Provide the LSE:minLSE ratio
** Provide a table of the weights, P's, and b's
** Organize and show the individual expression plots
** Interpret the results of the model

==== Coder/Designers ====

* Give a narrative description of the database, including the design of the expression tables and metadata table(s).
* Provide a database schema.

==== QA ====

* Describe the QA process for the database, noting any issues that were encountered, and stating whether data are complete or not.

=== Conclusions ===

* Write a '''1-2''' page conclusion that summarizes the overall project and your findings.
** Overall, what have you learned about the biological phenomenon described in your journal club article?
** How does the new database facilitate the data analysis?
** What future directions would you take if you were to continue this project?
** Relate the results of your project to the papers you presented for journal club and included in your bibliography in [[Week 11]].
*** Did you discover anything new that wasn't reported in the journal club paper (Data Analyst/QA)?

=== Acknowledgments ===

Write a short paragraph acknowledging the assistance of anyone who is not a member of your team.

=== References ===

* This section lists all of the references cited in the text of the report (and only those references cited in the paper). Follow the [[Media: BIOL367_Fall2019_GuidelinesforLiteratureCitations.pdf | Guidelines for Literature Citations in a Scientific Paper]] handout for general principles.
* Remember that you need to cite anything for which you are not the original source. Generally, in the introduction, you should aim for a minimum of two in-text citations per paragraph. You may reference the course web site using the appropriate format for a web reference.
* List your references in alphabetical order by first author using the [https://owl.purdue.edu/owl/research_and_citation/apa_style/apa_formatting_and_style_guide/reference_list_basic_rules.html APA style] that you have been using throughout the semester. Note that the proper format must be followed for both articles and websites, just URLs are not sufficient.

== PowerPoint Presentation ==

Each team of students will prepare and give a 20 minute PowerPoint presentation to report the results of their project on Tuesday, December 10 at 2:00-4:00 PM.
* Please follow the [http://brightspace.lmu.edu Presentation Guidelines on Brightspace] for how to format your slides.
* You will need to prepare ~20 slides (assume 1 slide per minute of presentation) and include the following content:
** Title slide that gives the main take-home message as the title of your presentation, the authors, date, and venue (course number and title).
** Outline slide that is a summary of take-home messages of your talk (should mirror your conclusion slide)
** The body of your talk (organized in a logical flow, not necessarily in the order given below).
*** Introduce the importance/significance of the problem and give background on the experiment in your journal article. You can draw from the your journal club presentation. This should follow the logic of the Introduction section of your group paper.
*** Show a combined flow chart of the tasks/milestones completed by your group (see Materials and Methods of group paper).
*** Show the table of ANOVA results.
*** Show the screenshot of the overall clustering results and the cluster you focused on.
*** Show a table of the GO results from that cluster, giving an interpretation.
*** Show the table of regulatory transcription factors in your network and their p values for enrichment.
*** Show the unweighted and weighted networks in GRNsight (with the genes arranged in the same way in each figure)
*** Database schema diagram for the MS Access database
** Conclusion slide that mirrors your outline
** Future directions
** Acknowledgments
** References
* '''''Your PowerPoint slides must be uploaded to the wiki and linked to from your individual journal page and your team page by 12:01am, Tuesday, December 10.'''''
** You can update your slides before your presentation, but we will be grading the ones you upload by the deadline.
* Your presentation (both the slides and the oral presentation) will be evaluated by the instructors using the [[Presentation Rubric]].
* Your presentation will also be evaluated by your fellow classmates (anonymously) who will answer the following questions:
*# What is the speaker's take-home message (one short sentence)?
*# What are the best points about the presentation's organization, visuals, and delivery? Please give at least 2 specific examples.
*# What points need improvement? Please give at least 2 specific examples.
* We expect that you will take the feedback from your previous presentation into account when doing this presentation.

== Individual Assessment and Reflection ==

Each person on the team will complete an assessment and reflection ''individually''. If you are comfortable with making this assessment publicly available, you may write it up as a wiki page or as a Word document uploaded to your group deliveables page. If you prefer to communicate your assessment privately, then email this to both Drs. Dahlquist and Dionisio.

=== Statement of Work ===

* Describe exactly what you did on the project.
* Provide references or links to artifacts of your work, such as:
** Wiki pages
** Other files or documents
** Code or scripts

=== Assessment of Project ===

* Give an objective assessment of the success of your project workflow and teamwork.
* What worked and what didn't work?
* What would you do differently if you could do it all over again?
* Evaluate your team’s portion of the GRNsight Gene Page Project and Group Report in the following areas:
*# Content: What is the quality of the work?
*# Organization: Comment on the organization of the project and of your group's wiki pages.
*# Completeness: Did your team achieve all of the project objectives? Why or why not?

=== Reflection on the Process ===

* What did you learn?
** With your head (biological or computer science principles)
** With your heart (personal qualities and teamwork qualities that make things work or not work)?
** With your hands (technical skills)?
* What lesson will you take away from this project that you will still use a year from now?

{{Final Project Links}}

[[Category:Group Projects]]

Final Project Deliverables

2019-11-19T23:15:05Z

Kdahlquist: /* Deliverables Checklist */ remove typo

{{Final Project Links}}

== Deliverables Checklist ==

# Organized Team deliverables wiki page (or other media (CD or flash drive) with table of contents)
# Group Report (''.doc'', ''.docx'' or ''.pdf'' file)
# Individual statements of work, assessments, reflections (wiki page, ''.doc'', ''.docx'', ''.pdf'', or e-mailed to Dr. Dahlquist)
# Group PowerPoint presentation (given on Tuesday, December 10, ''.ppt'', ''.pptx'' or ''.pdf'' file)
# Excel spreadsheet with ANOVA results/stem formatting (''.xlsx'')
# PowerPoint of ANOVA table, screenshots of stem results (''.pptx''), screenshot of black and white GRNsight input network and colored GRNsight output networks
# Gene List and GO List files from each significant profile (''.txt'' compressed together in a ''.zip'' file)
# YEASTRACT "rank by TF" results (''.xlsx'')
# GRNmap input workbook (with network adjacency matrix, ''.xlsx'')
# GRNmap output workbook (''.xlsx'') and output plots (''.jpg'') zipped together
# MS Access database, unified by the three teams with expression tables and metadata table(s) created (''.accdb'')
# ReadMe for the database that describes the design of the database, references the sources of the data, and has a database schema diagram (''.doc'', ''.docx'', ''.pdf'')
# Query design for populating a GRNmap input workbook from the database (screenshot of MS Access, or SQL code, ''.txt'')
# Electronic notebook corresponding to these the microarray results files ([[Week 12/13]] and [[Week 15]]) to support ''reproducible research'' so that all manipulations of the data and files are documented so that someone else could begin with your starting file, follow the protocol, and obtain your results.

=== Grading ===

* The Group Report is worth 50 points.
* The Group PowerPoint presentation is worth 50 points.
* All other deliverables listed above, together, are worth 50 points.

== Group Report ==

* The report should be written with contributions from all group members.
* Submit as ''.doc'', ''.docx'' or ''.pdf'' file.

=== Style Sheet ===

Use the following guidelines when formatting your report:
* 2.54 cm (1 in) margins on all sides
* Double-spaced
* 12 point Times/Times New Roman font
* Number the pages on the lower-right corner
* Use left justification (“jagged” on the right side)

=== Title Page ===

Include the following information in a standalone title page:
* A descriptive title for your project
** The function of the title is to identify the main result or take-home message of the paper. It should be as specific as possible. It can be a phrase or a sentence. What is the main result of your paper that you want to convey with the title?
* The names of the team members (with middle initials)
* The course number and title of the class
* The date of submission

=== Introduction ===

The introduction gives the background information necessary to understand your report and should be '''2-3''' pages long (double-spaced). The introduction should be in the form of a logical argument that “funnels” from broad to narrow:

<gallery mode="nolines" widths=322px heights=256px>
Funnel.jpg
</gallery>

* States importance of the problem
* States what is known about the problem
** Give an overview of what is known about the regulation of the transcriptional response in yeast from your team's journal article and the annotated bibliography your team developed.
** Discuss how you will approach this by re-analyzing the yeast microarray data from your article.
* States what is unknown about the problem
** Which transcription factors belong in the gene regulatory network that controls the response in yeast is still unknown.
* States clues that suggest how to approach the unknown
** Introduce GRNmap and GRNsight as the answer to this problem.
* States the question the paper is trying to address
** In this case you want to discover new information about the microarray data using GRNmap and GRNsight.
* State the approach
** Give an overview of your team's contribution to GRNsight and why it is needed.

=== Combined Methods/Results/Discussion section ===

This section will summarize the entire workflow and findings for the project with contributions from all team members (Data Analyst, QA, and Coder/Designers). Create a combined flow chart of the tasks/milestones of each of the team members and then briefly describe the flow chart in the text.
* Number each of the figures sequentially and number each of the tables sequentially in order from first mention in the text. You can either embed your figures and tables in the appropriate place in the text or put them all at the end. Do not mix both styles, however.
* Write a descriptive legend for each figure and table that briefly states what the figure/table is and gives a brief key to any labels and abbreviations.

==== Data Analyst ====

* Table of ANOVA results from the [[Week 8]], discussing the interpretation of the p values.
* From the STEM analyis, include as figures the overall results (the screenshot showing all of the clusters) and then focus on the ones you interpreted for the [[Week 10]] journal assignment.
** Include a table showing the GO results for that cluster (just the narrowed down list of terms that you have interpreted).
** Discuss what the p values for the cluster and for the GO term list mean.
** Discuss the biological interpretation of your GO terms.
* Include a table that lists the transcription factors in your final network and their enrichment p value from YEASTRACT.
** Describe how and why you and your partner chose these transcription factors for your network.
** Include a figure (screenshot) of the unweighted networks visualized with GRNsight.
* GRNmap results
** Show the GRNsight visualization (screenshot) of the weighted networks, making sure that the genes are placed in the same relative location as each other an as the unweighted network figure.
** Provide the LSE:minLSE ratio
** Provide a table of the weights, P's, and b's
** Organize and show the individual expression plots
** Interpret the results of the model

==== Coder/Designers ====

* Give a narrative description of the database, including the design of the expression tables and metadata table(s).
* Provide a database schema.

==== QA ====

* Describe the QA process for the database, noting any issues that were encountered, and stating whether data are complete or not.

=== Conclusions ===

* Write a '''1-2''' page conclusion that summarizes the overall project and your findings.
** Overall, what have you learned about the biological phenomenon described in your journal club article?
** How does the new database facilitate the data analysis?
** What future directions would you take if you were to continue this project?
** Relate the results of your project to the papers you presented for journal club and included in your bibliography in [[Week 11]].
*** Did you discover anything new that wasn't reported in the journal club paper (Data Analyst/QA)?

=== Acknowledgments ===

Write a short paragraph acknowledging the assistance of anyone who is not a member of your team.

=== References ===

* This section lists all of the references cited in the text of the report (and only those references cited in the paper). Follow the [[Media: BIOL367_Fall2019_GuidelinesforLiteratureCitations.pdf | Guidelines for Literature Citations in a Scientific Paper]] handout for general principles.
* Remember that you need to cite anything for which you are not the original source. Generally, in the introduction, you should aim for a minimum of two in-text citations per paragraph. You may reference the course web site using the appropriate format for a web reference.
* List your references in alphabetical order by first author using the [https://owl.purdue.edu/owl/research_and_citation/apa_style/apa_formatting_and_style_guide/reference_list_basic_rules.html APA style] that you have been using throughout the semester. Note that the proper format must be followed for both articles and websites, just URLs are not sufficient.

== PowerPoint Presentation ==

Each team of students will prepare and give a 20 minute PowerPoint presentation to report the results of their project on Tuesday, December 10 at 2:00-4:00 PM.
* Please follow the [http://brightspace.lmu.edu Presentation Guidelines on Brightspace] for how to format your slides.
* You will need to prepare ~20 slides (assume 1 slide per minute of presentation) and include the following content:
** Title slide that gives the main take-home message as the title of your presentation, the authors, date, and venue (course number and title).
** Outline slide that is a summary of take-home messages of your talk (should mirror your conclusion slide)
** The body of your talk (organized in a logical flow, not necessarily in the order given below).
*** Introduce the importance/significance of the problem and give background on the experiment in your journal article. You can draw from the DA/QA's journal club presentation. This should follow the logic of the Introduction section of your group paper.
*** Show a combined flow chart of the tasks/milestones completed by your group (see Materials and Methods of group paper).
*** Show the table of ANOVA results.
*** Show the screenshot of the overall clustering results and the cluster you focused on.
*** Show a table of the GO results from that cluster, giving an interpretation.
*** Show the table of regulatory transcription factors in your network and their p values for enrichment.
*** Show the unweighted and weighted networks in GRNsight (with the genes arranged in the same way in each figure)
*** Database schema diagram for the MS Access database
** Conclusion slide that mirrors your outline
** Future directions
** Acknowledgments
** References
* '''''Your PowerPoint slides must be uploaded to the wiki and linked to from your individual journal page and your team page by 12:01am, Tuesday, December 10.'''''
** You can update your slides before your presentation, but we will be grading the ones you upload by the deadline.
* Your presentation (both the slides and the oral presentation) will be evaluated by the instructors using the [[Presentation Rubric]].
* Your presentation will also be evaluated by your fellow classmates (anonymously) who will answer the following questions:
*# What is the speaker's take-home message (one short sentence)?
*# What are the best points about the presentation's organization, visuals, and delivery? Please give at least 2 specific examples.
*# What points need improvement? Please give at least 2 specific examples.
* We expect that you will take the feedback from your previous presentation into account when doing this presentation.

== Individual Assessment and Reflection ==

Each person on the team will complete an assessment and reflection ''individually''. If you are comfortable with making this assessment publicly available, you may write it up as a wiki page or as a Word document uploaded to your group deliveables page. If you prefer to communicate your assessment privately, then email this to both Drs. Dahlquist and Dionisio.

=== Statement of Work ===

* Describe exactly what you did on the project.
* Provide references or links to artifacts of your work, such as:
** Wiki pages
** Other files or documents
** Code or scripts

=== Assessment of Project ===

* Give an objective assessment of the success of your project workflow and teamwork.
* What worked and what didn't work?
* What would you do differently if you could do it all over again?
* Evaluate your team’s portion of the GRNsight Gene Page Project and Group Report in the following areas:
*# Content: What is the quality of the work?
*# Organization: Comment on the organization of the project and of your group's wiki pages.
*# Completeness: Did your team achieve all of the project objectives? Why or why not?

=== Reflection on the Process ===

* What did you learn?
** With your head (biological or computer science principles)
** With your heart (personal qualities and teamwork qualities that make things work or not work)?
** With your hands (technical skills)?
* What lesson will you take away from this project that you will still use a year from now?

{{Final Project Links}}

[[Category:Group Projects]]

Final Project Deliverables

2019-11-19T23:13:09Z

Kdahlquist: /* PowerPoint Presentation */ fix typo

{{Final Project Links}}

== Deliverables Checklist ==

# Organized Team deliverables wiki page (or other media (CD or flash drive) with table of contents)
# Group Report (''.doc'', ''.docx'' or ''.pdf'' file)
# Individual statements of work, assessments, reflections (wiki page, ''.doc'', ''.docx'', ''.pdf'', or e-mailed to Dr. Dahlquist)
# Group PowerPoint presentation (given on Tuesday, December 10, ''.ppt'', ''.pptx'' or ''.pdf'' file)
# Excel spreadsheet with ANOVA results/stem formatting (''.xlsx'')
# PowerPoint of ANOVA table, screenshots of stem results (''.pptx''), screenshot of black and white GRNsight input network and colored GRNsight output networks
# Gene List and GO List files from each significant profile (''.txt'' compressed together in a ''.zip'' file)
# YEASTRACT "rank by TF" results (''.xlsx'')
# GRNmap input workbook (with network adjacency matrix, ''.xlsx'')
# GRNmap output workbook (''.xlsx'') and output plots (''.jpg'') zipped together
# MS Access database, unified by the three teams with expression tables and metadata table(s) created (''.accdb'').
# ReadMe for the database that describes the design of the database, references the sources of the data, and has a database schema diagram (''.doc'', ''.docx'', ''.pdf'')
# Query design for populating a GRNmap input workbook from the database (screenshot of MS Access, or SQL code, ''.txt'')
# Electronic notebook corresponding to these the microarray results files ([[Week 12/13]] and [[Week 15]]) to support ''reproducible research'' so that all manipulations of the data and files are documented so that someone else could begin with your starting file, follow the protocol, and obtain your results.

=== Grading ===

* The Group Report is worth 50 points.
* The Group PowerPoint presentation is worth 50 points.
* All other deliverables listed above, together, are worth 50 points.

== Group Report ==

* The report should be written with contributions from all group members.
* Submit as ''.doc'', ''.docx'' or ''.pdf'' file.

=== Style Sheet ===

Use the following guidelines when formatting your report:
* 2.54 cm (1 in) margins on all sides
* Double-spaced
* 12 point Times/Times New Roman font
* Number the pages on the lower-right corner
* Use left justification (“jagged” on the right side)

=== Title Page ===

Include the following information in a standalone title page:
* A descriptive title for your project
** The function of the title is to identify the main result or take-home message of the paper. It should be as specific as possible. It can be a phrase or a sentence. What is the main result of your paper that you want to convey with the title?
* The names of the team members (with middle initials)
* The course number and title of the class
* The date of submission

=== Introduction ===

The introduction gives the background information necessary to understand your report and should be '''2-3''' pages long (double-spaced). The introduction should be in the form of a logical argument that “funnels” from broad to narrow:

<gallery mode="nolines" widths=322px heights=256px>
Funnel.jpg
</gallery>

* States importance of the problem
* States what is known about the problem
** Give an overview of what is known about the regulation of the transcriptional response in yeast from your team's journal article and the annotated bibliography your team developed.
** Discuss how you will approach this by re-analyzing the yeast microarray data from your article.
* States what is unknown about the problem
** Which transcription factors belong in the gene regulatory network that controls the response in yeast is still unknown.
* States clues that suggest how to approach the unknown
** Introduce GRNmap and GRNsight as the answer to this problem.
* States the question the paper is trying to address
** In this case you want to discover new information about the microarray data using GRNmap and GRNsight.
* State the approach
** Give an overview of your team's contribution to GRNsight and why it is needed.

=== Combined Methods/Results/Discussion section ===

This section will summarize the entire workflow and findings for the project with contributions from all team members (Data Analyst, QA, and Coder/Designers). Create a combined flow chart of the tasks/milestones of each of the team members and then briefly describe the flow chart in the text.
* Number each of the figures sequentially and number each of the tables sequentially in order from first mention in the text. You can either embed your figures and tables in the appropriate place in the text or put them all at the end. Do not mix both styles, however.
* Write a descriptive legend for each figure and table that briefly states what the figure/table is and gives a brief key to any labels and abbreviations.

==== Data Analyst ====

* Table of ANOVA results from the [[Week 8]], discussing the interpretation of the p values.
* From the STEM analyis, include as figures the overall results (the screenshot showing all of the clusters) and then focus on the ones you interpreted for the [[Week 10]] journal assignment.
** Include a table showing the GO results for that cluster (just the narrowed down list of terms that you have interpreted).
** Discuss what the p values for the cluster and for the GO term list mean.
** Discuss the biological interpretation of your GO terms.
* Include a table that lists the transcription factors in your final network and their enrichment p value from YEASTRACT.
** Describe how and why you and your partner chose these transcription factors for your network.
** Include a figure (screenshot) of the unweighted networks visualized with GRNsight.
* GRNmap results
** Show the GRNsight visualization (screenshot) of the weighted networks, making sure that the genes are placed in the same relative location as each other an as the unweighted network figure.
** Provide the LSE:minLSE ratio
** Provide a table of the weights, P's, and b's
** Organize and show the individual expression plots
** Interpret the results of the model

==== Coder/Designers ====

* Give a narrative description of the database, including the design of the expression tables and metadata table(s).
* Provide a database schema.

==== QA ====

* Describe the QA process for the database, noting any issues that were encountered, and stating whether data are complete or not.

=== Conclusions ===

* Write a '''1-2''' page conclusion that summarizes the overall project and your findings.
** Overall, what have you learned about the biological phenomenon described in your journal club article?
** How does the new database facilitate the data analysis?
** What future directions would you take if you were to continue this project?
** Relate the results of your project to the papers you presented for journal club and included in your bibliography in [[Week 11]].
*** Did you discover anything new that wasn't reported in the journal club paper (Data Analyst/QA)?

=== Acknowledgments ===

Write a short paragraph acknowledging the assistance of anyone who is not a member of your team.

=== References ===

* This section lists all of the references cited in the text of the report (and only those references cited in the paper). Follow the [[Media: BIOL367_Fall2019_GuidelinesforLiteratureCitations.pdf | Guidelines for Literature Citations in a Scientific Paper]] handout for general principles.
* Remember that you need to cite anything for which you are not the original source. Generally, in the introduction, you should aim for a minimum of two in-text citations per paragraph. You may reference the course web site using the appropriate format for a web reference.
* List your references in alphabetical order by first author using the [https://owl.purdue.edu/owl/research_and_citation/apa_style/apa_formatting_and_style_guide/reference_list_basic_rules.html APA style] that you have been using throughout the semester. Note that the proper format must be followed for both articles and websites, just URLs are not sufficient.

== PowerPoint Presentation ==

Each team of students will prepare and give a 20 minute PowerPoint presentation to report the results of their project on Tuesday, December 10 at 2:00-4:00 PM.
* Please follow the [http://brightspace.lmu.edu Presentation Guidelines on Brightspace] for how to format your slides.
* You will need to prepare ~20 slides (assume 1 slide per minute of presentation) and include the following content:
** Title slide that gives the main take-home message as the title of your presentation, the authors, date, and venue (course number and title).
** Outline slide that is a summary of take-home messages of your talk (should mirror your conclusion slide)
** The body of your talk (organized in a logical flow, not necessarily in the order given below).
*** Introduce the importance/significance of the problem and give background on the experiment in your journal article. You can draw from the DA/QA's journal club presentation. This should follow the logic of the Introduction section of your group paper.
*** Show a combined flow chart of the tasks/milestones completed by your group (see Materials and Methods of group paper).
*** Show the table of ANOVA results.
*** Show the screenshot of the overall clustering results and the cluster you focused on.
*** Show a table of the GO results from that cluster, giving an interpretation.
*** Show the table of regulatory transcription factors in your network and their p values for enrichment.
*** Show the unweighted and weighted networks in GRNsight (with the genes arranged in the same way in each figure)
*** Database schema diagram for the MS Access database
** Conclusion slide that mirrors your outline
** Future directions
** Acknowledgments
** References
* '''''Your PowerPoint slides must be uploaded to the wiki and linked to from your individual journal page and your team page by 12:01am, Tuesday, December 10.'''''
** You can update your slides before your presentation, but we will be grading the ones you upload by the deadline.
* Your presentation (both the slides and the oral presentation) will be evaluated by the instructors using the [[Presentation Rubric]].
* Your presentation will also be evaluated by your fellow classmates (anonymously) who will answer the following questions:
*# What is the speaker's take-home message (one short sentence)?
*# What are the best points about the presentation's organization, visuals, and delivery? Please give at least 2 specific examples.
*# What points need improvement? Please give at least 2 specific examples.
* We expect that you will take the feedback from your previous presentation into account when doing this presentation.

== Individual Assessment and Reflection ==

Each person on the team will complete an assessment and reflection ''individually''. If you are comfortable with making this assessment publicly available, you may write it up as a wiki page or as a Word document uploaded to your group deliveables page. If you prefer to communicate your assessment privately, then email this to both Drs. Dahlquist and Dionisio.

=== Statement of Work ===

* Describe exactly what you did on the project.
* Provide references or links to artifacts of your work, such as:
** Wiki pages
** Other files or documents
** Code or scripts

=== Assessment of Project ===

* Give an objective assessment of the success of your project workflow and teamwork.
* What worked and what didn't work?
* What would you do differently if you could do it all over again?
* Evaluate your team’s portion of the GRNsight Gene Page Project and Group Report in the following areas:
*# Content: What is the quality of the work?
*# Organization: Comment on the organization of the project and of your group's wiki pages.
*# Completeness: Did your team achieve all of the project objectives? Why or why not?

=== Reflection on the Process ===

* What did you learn?
** With your head (biological or computer science principles)
** With your heart (personal qualities and teamwork qualities that make things work or not work)?
** With your hands (technical skills)?
* What lesson will you take away from this project that you will still use a year from now?

{{Final Project Links}}

[[Category:Group Projects]]

Final Project Deliverables

2019-11-19T23:12:54Z

Kdahlquist: /* PowerPoint Presentation */ remove reference to cold shock

{{Final Project Links}}

== Deliverables Checklist ==

# Organized Team deliverables wiki page (or other media (CD or flash drive) with table of contents)
# Group Report (''.doc'', ''.docx'' or ''.pdf'' file)
# Individual statements of work, assessments, reflections (wiki page, ''.doc'', ''.docx'', ''.pdf'', or e-mailed to Dr. Dahlquist)
# Group PowerPoint presentation (given on Tuesday, December 10, ''.ppt'', ''.pptx'' or ''.pdf'' file)
# Excel spreadsheet with ANOVA results/stem formatting (''.xlsx'')
# PowerPoint of ANOVA table, screenshots of stem results (''.pptx''), screenshot of black and white GRNsight input network and colored GRNsight output networks
# Gene List and GO List files from each significant profile (''.txt'' compressed together in a ''.zip'' file)
# YEASTRACT "rank by TF" results (''.xlsx'')
# GRNmap input workbook (with network adjacency matrix, ''.xlsx'')
# GRNmap output workbook (''.xlsx'') and output plots (''.jpg'') zipped together
# MS Access database, unified by the three teams with expression tables and metadata table(s) created (''.accdb'').
# ReadMe for the database that describes the design of the database, references the sources of the data, and has a database schema diagram (''.doc'', ''.docx'', ''.pdf'')
# Query design for populating a GRNmap input workbook from the database (screenshot of MS Access, or SQL code, ''.txt'')
# Electronic notebook corresponding to these the microarray results files ([[Week 12/13]] and [[Week 15]]) to support ''reproducible research'' so that all manipulations of the data and files are documented so that someone else could begin with your starting file, follow the protocol, and obtain your results.

=== Grading ===

* The Group Report is worth 50 points.
* The Group PowerPoint presentation is worth 50 points.
* All other deliverables listed above, together, are worth 50 points.

== Group Report ==

* The report should be written with contributions from all group members.
* Submit as ''.doc'', ''.docx'' or ''.pdf'' file.

=== Style Sheet ===

Use the following guidelines when formatting your report:
* 2.54 cm (1 in) margins on all sides
* Double-spaced
* 12 point Times/Times New Roman font
* Number the pages on the lower-right corner
* Use left justification (“jagged” on the right side)

=== Title Page ===

Include the following information in a standalone title page:
* A descriptive title for your project
** The function of the title is to identify the main result or take-home message of the paper. It should be as specific as possible. It can be a phrase or a sentence. What is the main result of your paper that you want to convey with the title?
* The names of the team members (with middle initials)
* The course number and title of the class
* The date of submission

=== Introduction ===

The introduction gives the background information necessary to understand your report and should be '''2-3''' pages long (double-spaced). The introduction should be in the form of a logical argument that “funnels” from broad to narrow:

<gallery mode="nolines" widths=322px heights=256px>
Funnel.jpg
</gallery>

* States importance of the problem
* States what is known about the problem
** Give an overview of what is known about the regulation of the transcriptional response in yeast from your team's journal article and the annotated bibliography your team developed.
** Discuss how you will approach this by re-analyzing the yeast microarray data from your article.
* States what is unknown about the problem
** Which transcription factors belong in the gene regulatory network that controls the response in yeast is still unknown.
* States clues that suggest how to approach the unknown
** Introduce GRNmap and GRNsight as the answer to this problem.
* States the question the paper is trying to address
** In this case you want to discover new information about the microarray data using GRNmap and GRNsight.
* State the approach
** Give an overview of your team's contribution to GRNsight and why it is needed.

=== Combined Methods/Results/Discussion section ===

This section will summarize the entire workflow and findings for the project with contributions from all team members (Data Analyst, QA, and Coder/Designers). Create a combined flow chart of the tasks/milestones of each of the team members and then briefly describe the flow chart in the text.
* Number each of the figures sequentially and number each of the tables sequentially in order from first mention in the text. You can either embed your figures and tables in the appropriate place in the text or put them all at the end. Do not mix both styles, however.
* Write a descriptive legend for each figure and table that briefly states what the figure/table is and gives a brief key to any labels and abbreviations.

==== Data Analyst ====

* Table of ANOVA results from the [[Week 8]], discussing the interpretation of the p values.
* From the STEM analyis, include as figures the overall results (the screenshot showing all of the clusters) and then focus on the ones you interpreted for the [[Week 10]] journal assignment.
** Include a table showing the GO results for that cluster (just the narrowed down list of terms that you have interpreted).
** Discuss what the p values for the cluster and for the GO term list mean.
** Discuss the biological interpretation of your GO terms.
* Include a table that lists the transcription factors in your final network and their enrichment p value from YEASTRACT.
** Describe how and why you and your partner chose these transcription factors for your network.
** Include a figure (screenshot) of the unweighted networks visualized with GRNsight.
* GRNmap results
** Show the GRNsight visualization (screenshot) of the weighted networks, making sure that the genes are placed in the same relative location as each other an as the unweighted network figure.
** Provide the LSE:minLSE ratio
** Provide a table of the weights, P's, and b's
** Organize and show the individual expression plots
** Interpret the results of the model

==== Coder/Designers ====

* Give a narrative description of the database, including the design of the expression tables and metadata table(s).
* Provide a database schema.

==== QA ====

* Describe the QA process for the database, noting any issues that were encountered, and stating whether data are complete or not.

=== Conclusions ===

* Write a '''1-2''' page conclusion that summarizes the overall project and your findings.
** Overall, what have you learned about the biological phenomenon described in your journal club article?
** How does the new database facilitate the data analysis?
** What future directions would you take if you were to continue this project?
** Relate the results of your project to the papers you presented for journal club and included in your bibliography in [[Week 11]].
*** Did you discover anything new that wasn't reported in the journal club paper (Data Analyst/QA)?

=== Acknowledgments ===

Write a short paragraph acknowledging the assistance of anyone who is not a member of your team.

=== References ===

* This section lists all of the references cited in the text of the report (and only those references cited in the paper). Follow the [[Media: BIOL367_Fall2019_GuidelinesforLiteratureCitations.pdf | Guidelines for Literature Citations in a Scientific Paper]] handout for general principles.
* Remember that you need to cite anything for which you are not the original source. Generally, in the introduction, you should aim for a minimum of two in-text citations per paragraph. You may reference the course web site using the appropriate format for a web reference.
* List your references in alphabetical order by first author using the [https://owl.purdue.edu/owl/research_and_citation/apa_style/apa_formatting_and_style_guide/reference_list_basic_rules.html APA style] that you have been using throughout the semester. Note that the proper format must be followed for both articles and websites, just URLs are not sufficient.

== PowerPoint Presentation ==

Each team of students will prepare and give a 20 minute PowerPoint presentation to report the results of their project on Tuesday, December 10 at 2:00-4:00 PM.
* Please follow the [http://brightspace.lmu.edu Presentation Guidelines on Brightspace] for how to format your slides.
* You will need to prepare ~20 slides (assume 1 slide per minute of presentation) and include the following content:
** Title slide that gives the main take-home message as the title of your presentation, the authors, date, and venue (course number and title).
** Outline slide that is a summary of take-home messages of your talk (should mirror your conclusion slide)
** The body of your talk (organized in a logical flow, not necessarily in the order given below).
*** Introduce the importance/significance of the problem and give background on the experiment in your journal article. You can draw from the DA/QA's journal club presentation. This should follow the logic of the Introduction section of your group paper.
*** Show a combined flow chart of the tasks/milestones completed by your group (see Materials and Methods of group paper).
*** Show the table of ANOVA results.
*** Show the screenshot of the overall clustering results and the cluster you focused on .
*** Show a table of the GO results from that cluster, giving an interpretation.
*** Show the table of regulatory transcription factors in your network and their p values for enrichment.
*** Show the unweighted and weighted networks in GRNsight (with the genes arranged in the same way in each figure)
*** Database schema diagram for the MS Access database
** Conclusion slide that mirrors your outline
** Future directions
** Acknowledgments
** References
* '''''Your PowerPoint slides must be uploaded to the wiki and linked to from your individual journal page and your team page by 12:01am, Tuesday, December 10.'''''
** You can update your slides before your presentation, but we will be grading the ones you upload by the deadline.
* Your presentation (both the slides and the oral presentation) will be evaluated by the instructors using the [[Presentation Rubric]].
* Your presentation will also be evaluated by your fellow classmates (anonymously) who will answer the following questions:
*# What is the speaker's take-home message (one short sentence)?
*# What are the best points about the presentation's organization, visuals, and delivery? Please give at least 2 specific examples.
*# What points need improvement? Please give at least 2 specific examples.
* We expect that you will take the feedback from your previous presentation into account when doing this presentation.

== Individual Assessment and Reflection ==

Each person on the team will complete an assessment and reflection ''individually''. If you are comfortable with making this assessment publicly available, you may write it up as a wiki page or as a Word document uploaded to your group deliveables page. If you prefer to communicate your assessment privately, then email this to both Drs. Dahlquist and Dionisio.

=== Statement of Work ===

* Describe exactly what you did on the project.
* Provide references or links to artifacts of your work, such as:
** Wiki pages
** Other files or documents
** Code or scripts

=== Assessment of Project ===

* Give an objective assessment of the success of your project workflow and teamwork.
* What worked and what didn't work?
* What would you do differently if you could do it all over again?
* Evaluate your team’s portion of the GRNsight Gene Page Project and Group Report in the following areas:
*# Content: What is the quality of the work?
*# Organization: Comment on the organization of the project and of your group's wiki pages.
*# Completeness: Did your team achieve all of the project objectives? Why or why not?

=== Reflection on the Process ===

* What did you learn?
** With your head (biological or computer science principles)
** With your heart (personal qualities and teamwork qualities that make things work or not work)?
** With your hands (technical skills)?
* What lesson will you take away from this project that you will still use a year from now?

{{Final Project Links}}

[[Category:Group Projects]]

Final Project Deliverables

2019-11-19T23:11:57Z

Kdahlquist: /* Conclusions */ removed reference to cold shock

{{Final Project Links}}

== Deliverables Checklist ==

# Organized Team deliverables wiki page (or other media (CD or flash drive) with table of contents)
# Group Report (''.doc'', ''.docx'' or ''.pdf'' file)
# Individual statements of work, assessments, reflections (wiki page, ''.doc'', ''.docx'', ''.pdf'', or e-mailed to Dr. Dahlquist)
# Group PowerPoint presentation (given on Tuesday, December 10, ''.ppt'', ''.pptx'' or ''.pdf'' file)
# Excel spreadsheet with ANOVA results/stem formatting (''.xlsx'')
# PowerPoint of ANOVA table, screenshots of stem results (''.pptx''), screenshot of black and white GRNsight input network and colored GRNsight output networks
# Gene List and GO List files from each significant profile (''.txt'' compressed together in a ''.zip'' file)
# YEASTRACT "rank by TF" results (''.xlsx'')
# GRNmap input workbook (with network adjacency matrix, ''.xlsx'')
# GRNmap output workbook (''.xlsx'') and output plots (''.jpg'') zipped together
# MS Access database, unified by the three teams with expression tables and metadata table(s) created (''.accdb'').
# ReadMe for the database that describes the design of the database, references the sources of the data, and has a database schema diagram (''.doc'', ''.docx'', ''.pdf'')
# Query design for populating a GRNmap input workbook from the database (screenshot of MS Access, or SQL code, ''.txt'')
# Electronic notebook corresponding to these the microarray results files ([[Week 12/13]] and [[Week 15]]) to support ''reproducible research'' so that all manipulations of the data and files are documented so that someone else could begin with your starting file, follow the protocol, and obtain your results.

=== Grading ===

* The Group Report is worth 50 points.
* The Group PowerPoint presentation is worth 50 points.
* All other deliverables listed above, together, are worth 50 points.

== Group Report ==

* The report should be written with contributions from all group members.
* Submit as ''.doc'', ''.docx'' or ''.pdf'' file.

=== Style Sheet ===

Use the following guidelines when formatting your report:
* 2.54 cm (1 in) margins on all sides
* Double-spaced
* 12 point Times/Times New Roman font
* Number the pages on the lower-right corner
* Use left justification (“jagged” on the right side)

=== Title Page ===

Include the following information in a standalone title page:
* A descriptive title for your project
** The function of the title is to identify the main result or take-home message of the paper. It should be as specific as possible. It can be a phrase or a sentence. What is the main result of your paper that you want to convey with the title?
* The names of the team members (with middle initials)
* The course number and title of the class
* The date of submission

=== Introduction ===

The introduction gives the background information necessary to understand your report and should be '''2-3''' pages long (double-spaced). The introduction should be in the form of a logical argument that “funnels” from broad to narrow:

<gallery mode="nolines" widths=322px heights=256px>
Funnel.jpg
</gallery>

* States importance of the problem
* States what is known about the problem
** Give an overview of what is known about the regulation of the transcriptional response in yeast from your team's journal article and the annotated bibliography your team developed.
** Discuss how you will approach this by re-analyzing the yeast microarray data from your article.
* States what is unknown about the problem
** Which transcription factors belong in the gene regulatory network that controls the response in yeast is still unknown.
* States clues that suggest how to approach the unknown
** Introduce GRNmap and GRNsight as the answer to this problem.
* States the question the paper is trying to address
** In this case you want to discover new information about the microarray data using GRNmap and GRNsight.
* State the approach
** Give an overview of your team's contribution to GRNsight and why it is needed.

=== Combined Methods/Results/Discussion section ===

This section will summarize the entire workflow and findings for the project with contributions from all team members (Data Analyst, QA, and Coder/Designers). Create a combined flow chart of the tasks/milestones of each of the team members and then briefly describe the flow chart in the text.
* Number each of the figures sequentially and number each of the tables sequentially in order from first mention in the text. You can either embed your figures and tables in the appropriate place in the text or put them all at the end. Do not mix both styles, however.
* Write a descriptive legend for each figure and table that briefly states what the figure/table is and gives a brief key to any labels and abbreviations.

==== Data Analyst ====

* Table of ANOVA results from the [[Week 8]], discussing the interpretation of the p values.
* From the STEM analyis, include as figures the overall results (the screenshot showing all of the clusters) and then focus on the ones you interpreted for the [[Week 10]] journal assignment.
** Include a table showing the GO results for that cluster (just the narrowed down list of terms that you have interpreted).
** Discuss what the p values for the cluster and for the GO term list mean.
** Discuss the biological interpretation of your GO terms.
* Include a table that lists the transcription factors in your final network and their enrichment p value from YEASTRACT.
** Describe how and why you and your partner chose these transcription factors for your network.
** Include a figure (screenshot) of the unweighted networks visualized with GRNsight.
* GRNmap results
** Show the GRNsight visualization (screenshot) of the weighted networks, making sure that the genes are placed in the same relative location as each other an as the unweighted network figure.
** Provide the LSE:minLSE ratio
** Provide a table of the weights, P's, and b's
** Organize and show the individual expression plots
** Interpret the results of the model

==== Coder/Designers ====

* Give a narrative description of the database, including the design of the expression tables and metadata table(s).
* Provide a database schema.

==== QA ====

* Describe the QA process for the database, noting any issues that were encountered, and stating whether data are complete or not.

=== Conclusions ===

* Write a '''1-2''' page conclusion that summarizes the overall project and your findings.
** Overall, what have you learned about the biological phenomenon described in your journal club article?
** How does the new database facilitate the data analysis?
** What future directions would you take if you were to continue this project?
** Relate the results of your project to the papers you presented for journal club and included in your bibliography in [[Week 11]].
*** Did you discover anything new that wasn't reported in the journal club paper (Data Analyst/QA)?

=== Acknowledgments ===

Write a short paragraph acknowledging the assistance of anyone who is not a member of your team.

=== References ===

* This section lists all of the references cited in the text of the report (and only those references cited in the paper). Follow the [[Media: BIOL367_Fall2019_GuidelinesforLiteratureCitations.pdf | Guidelines for Literature Citations in a Scientific Paper]] handout for general principles.
* Remember that you need to cite anything for which you are not the original source. Generally, in the introduction, you should aim for a minimum of two in-text citations per paragraph. You may reference the course web site using the appropriate format for a web reference.
* List your references in alphabetical order by first author using the [https://owl.purdue.edu/owl/research_and_citation/apa_style/apa_formatting_and_style_guide/reference_list_basic_rules.html APA style] that you have been using throughout the semester. Note that the proper format must be followed for both articles and websites, just URLs are not sufficient.

== PowerPoint Presentation ==

Each team of students will prepare and give a 20 minute PowerPoint presentation to report the results of their project on Tuesday, December 10 at 2:00-4:00 PM.
* Please follow the [http://brightspace.lmu.edu Presentation Guidelines on Brightspace] for how to format your slides.
* You will need to prepare ~20 slides (assume 1 slide per minute of presentation) and include the following content:
** Title slide that gives the main take-home message as the title of your presentation, the authors, date, and venue (course number and title).
** Outline slide that is a summary of take-home messages of your talk (should mirror your conclusion slide)
** The body of your talk (organized in a logical flow, not necessarily in the order given below).
*** Introduce the importance/significance of the problem and give background on cold shock in yeast. You can draw from the DA/QA's journal club presentation. This should follow the logic of the Introduction section of your group paper.
*** Show a combined flow chart of the tasks/milestones completed by your group (see Materials and Methods of group paper).
*** Show the table of ANOVA results.
*** Show the screenshot of the overall clustering results and the cluster you focused on .
*** Show a table of the GO results from that cluster, giving an interpretation.
*** Show the table of regulatory transcription factors in your network and their p values for enrichment.
*** Show the unweighted and weighted networks in GRNsight (with the genes arranged in the same way in each figure)
*** Database schema diagram for the MS Access database
** Conclusion slide that mirrors your outline
** Future directions
** Acknowledgments
** References
* '''''Your PowerPoint slides must be uploaded to the wiki and linked to from your individual journal page and your team page by 12:01am, Tuesday, December 10.'''''
** You can update your slides before your presentation, but we will be grading the ones you upload by the deadline.
* Your presentation (both the slides and the oral presentation) will be evaluated by the instructors using the [[Presentation Rubric]].
* Your presentation will also be evaluated by your fellow classmates (anonymously) who will answer the following questions:
*# What is the speaker's take-home message (one short sentence)?
*# What are the best points about the presentation's organization, visuals, and delivery? Please give at least 2 specific examples.
*# What points need improvement? Please give at least 2 specific examples.
* We expect that you will take the feedback from your previous presentation into account when doing this presentation.

== Individual Assessment and Reflection ==

Each person on the team will complete an assessment and reflection ''individually''. If you are comfortable with making this assessment publicly available, you may write it up as a wiki page or as a Word document uploaded to your group deliveables page. If you prefer to communicate your assessment privately, then email this to both Drs. Dahlquist and Dionisio.

=== Statement of Work ===

* Describe exactly what you did on the project.
* Provide references or links to artifacts of your work, such as:
** Wiki pages
** Other files or documents
** Code or scripts

=== Assessment of Project ===

* Give an objective assessment of the success of your project workflow and teamwork.
* What worked and what didn't work?
* What would you do differently if you could do it all over again?
* Evaluate your team’s portion of the GRNsight Gene Page Project and Group Report in the following areas:
*# Content: What is the quality of the work?
*# Organization: Comment on the organization of the project and of your group's wiki pages.
*# Completeness: Did your team achieve all of the project objectives? Why or why not?

=== Reflection on the Process ===

* What did you learn?
** With your head (biological or computer science principles)
** With your heart (personal qualities and teamwork qualities that make things work or not work)?
** With your hands (technical skills)?
* What lesson will you take away from this project that you will still use a year from now?

{{Final Project Links}}

[[Category:Group Projects]]

Final Project Deliverables

2019-11-19T22:50:10Z

Kdahlquist: remove under construction

{{Final Project Links}}

== Deliverables Checklist ==

# Organized Team deliverables wiki page (or other media (CD or flash drive) with table of contents)
# Group Report (''.doc'', ''.docx'' or ''.pdf'' file)
# Individual statements of work, assessments, reflections (wiki page, ''.doc'', ''.docx'', ''.pdf'', or e-mailed to Dr. Dahlquist)
# Group PowerPoint presentation (given on Tuesday, December 10, ''.ppt'', ''.pptx'' or ''.pdf'' file)
# Excel spreadsheet with ANOVA results/stem formatting (''.xlsx'')
# PowerPoint of ANOVA table, screenshots of stem results (''.pptx''), screenshot of black and white GRNsight input network and colored GRNsight output networks
# Gene List and GO List files from each significant profile (''.txt'' compressed together in a ''.zip'' file)
# YEASTRACT "rank by TF" results (''.xlsx'')
# GRNmap input workbook (with network adjacency matrix, ''.xlsx'')
# GRNmap output workbook (''.xlsx'') and output plots (''.jpg'') zipped together
# MS Access database, unified by the three teams with expression tables and metadata table(s) created (''.accdb'').
# ReadMe for the database that describes the design of the database, references the sources of the data, and has a database schema diagram (''.doc'', ''.docx'', ''.pdf'')
# Query design for populating a GRNmap input workbook from the database (screenshot of MS Access, or SQL code, ''.txt'')
# Electronic notebook corresponding to these the microarray results files ([[Week 12/13]] and [[Week 15]]) to support ''reproducible research'' so that all manipulations of the data and files are documented so that someone else could begin with your starting file, follow the protocol, and obtain your results.

=== Grading ===

* The Group Report is worth 50 points.
* The Group PowerPoint presentation is worth 50 points.
* All other deliverables listed above, together, are worth 50 points.

== Group Report ==

* The report should be written with contributions from all group members.
* Submit as ''.doc'', ''.docx'' or ''.pdf'' file.

=== Style Sheet ===

Use the following guidelines when formatting your report:
* 2.54 cm (1 in) margins on all sides
* Double-spaced
* 12 point Times/Times New Roman font
* Number the pages on the lower-right corner
* Use left justification (“jagged” on the right side)

=== Title Page ===

Include the following information in a standalone title page:
* A descriptive title for your project
** The function of the title is to identify the main result or take-home message of the paper. It should be as specific as possible. It can be a phrase or a sentence. What is the main result of your paper that you want to convey with the title?
* The names of the team members (with middle initials)
* The course number and title of the class
* The date of submission

=== Introduction ===

The introduction gives the background information necessary to understand your report and should be '''2-3''' pages long (double-spaced). The introduction should be in the form of a logical argument that “funnels” from broad to narrow:

<gallery mode="nolines" widths=322px heights=256px>
Funnel.jpg
</gallery>

* States importance of the problem
* States what is known about the problem
** Give an overview of what is known about the regulation of the transcriptional response in yeast from your team's journal article and the annotated bibliography your team developed.
** Discuss how you will approach this by re-analyzing the yeast microarray data from your article.
* States what is unknown about the problem
** Which transcription factors belong in the gene regulatory network that controls the response in yeast is still unknown.
* States clues that suggest how to approach the unknown
** Introduce GRNmap and GRNsight as the answer to this problem.
* States the question the paper is trying to address
** In this case you want to discover new information about the microarray data using GRNmap and GRNsight.
* State the approach
** Give an overview of your team's contribution to GRNsight and why it is needed.

=== Combined Methods/Results/Discussion section ===

This section will summarize the entire workflow and findings for the project with contributions from all team members (Data Analyst, QA, and Coder/Designers). Create a combined flow chart of the tasks/milestones of each of the team members and then briefly describe the flow chart in the text.
* Number each of the figures sequentially and number each of the tables sequentially in order from first mention in the text. You can either embed your figures and tables in the appropriate place in the text or put them all at the end. Do not mix both styles, however.
* Write a descriptive legend for each figure and table that briefly states what the figure/table is and gives a brief key to any labels and abbreviations.

==== Data Analyst ====

* Table of ANOVA results from the [[Week 8]], discussing the interpretation of the p values.
* From the STEM analyis, include as figures the overall results (the screenshot showing all of the clusters) and then focus on the ones you interpreted for the [[Week 10]] journal assignment.
** Include a table showing the GO results for that cluster (just the narrowed down list of terms that you have interpreted).
** Discuss what the p values for the cluster and for the GO term list mean.
** Discuss the biological interpretation of your GO terms.
* Include a table that lists the transcription factors in your final network and their enrichment p value from YEASTRACT.
** Describe how and why you and your partner chose these transcription factors for your network.
** Include a figure (screenshot) of the unweighted networks visualized with GRNsight.
* GRNmap results
** Show the GRNsight visualization (screenshot) of the weighted networks, making sure that the genes are placed in the same relative location as each other an as the unweighted network figure.
** Provide the LSE:minLSE ratio
** Provide a table of the weights, P's, and b's
** Organize and show the individual expression plots
** Interpret the results of the model

==== Coder/Designers ====

* Give a narrative description of the database, including the design of the expression tables and metadata table(s).
* Provide a database schema.

==== QA ====

* Describe the QA process for the database, noting any issues that were encountered, and stating whether data are complete or not.

=== Conclusions ===

* Write a '''1-2''' page conclusion that summarizes the overall project and your findings.
** Overall, what have you learned about the regulation of cold shock in yeast?
** How does the new gene page feature facilitate the data analysis?
** What future directions would you take if you were to continue this project?
** Relate the results of your project to the papers you presented for journal club and included in your bibliography in [[Week 11]].
*** Did you discover anything new that wasn't reported in the journal club paper (Data Analyst/QA)?

=== Acknowledgments ===

Write a short paragraph acknowledging the assistance of anyone who is not a member of your team.

=== References ===

* This section lists all of the references cited in the text of the report (and only those references cited in the paper). Follow the [[Media: BIOL367_Fall2019_GuidelinesforLiteratureCitations.pdf | Guidelines for Literature Citations in a Scientific Paper]] handout for general principles.
* Remember that you need to cite anything for which you are not the original source. Generally, in the introduction, you should aim for a minimum of two in-text citations per paragraph. You may reference the course web site using the appropriate format for a web reference.
* List your references in alphabetical order by first author using the [https://owl.purdue.edu/owl/research_and_citation/apa_style/apa_formatting_and_style_guide/reference_list_basic_rules.html APA style] that you have been using throughout the semester. Note that the proper format must be followed for both articles and websites, just URLs are not sufficient.

== PowerPoint Presentation ==

Each team of students will prepare and give a 20 minute PowerPoint presentation to report the results of their project on Tuesday, December 10 at 2:00-4:00 PM.
* Please follow the [http://brightspace.lmu.edu Presentation Guidelines on Brightspace] for how to format your slides.
* You will need to prepare ~20 slides (assume 1 slide per minute of presentation) and include the following content:
** Title slide that gives the main take-home message as the title of your presentation, the authors, date, and venue (course number and title).
** Outline slide that is a summary of take-home messages of your talk (should mirror your conclusion slide)
** The body of your talk (organized in a logical flow, not necessarily in the order given below).
*** Introduce the importance/significance of the problem and give background on cold shock in yeast. You can draw from the DA/QA's journal club presentation. This should follow the logic of the Introduction section of your group paper.
*** Show a combined flow chart of the tasks/milestones completed by your group (see Materials and Methods of group paper).
*** Show the table of ANOVA results.
*** Show the screenshot of the overall clustering results and the cluster you focused on .
*** Show a table of the GO results from that cluster, giving an interpretation.
*** Show the table of regulatory transcription factors in your network and their p values for enrichment.
*** Show the unweighted and weighted networks in GRNsight (with the genes arranged in the same way in each figure)
*** Database schema diagram for the MS Access database
** Conclusion slide that mirrors your outline
** Future directions
** Acknowledgments
** References
* '''''Your PowerPoint slides must be uploaded to the wiki and linked to from your individual journal page and your team page by 12:01am, Tuesday, December 10.'''''
** You can update your slides before your presentation, but we will be grading the ones you upload by the deadline.
* Your presentation (both the slides and the oral presentation) will be evaluated by the instructors using the [[Presentation Rubric]].
* Your presentation will also be evaluated by your fellow classmates (anonymously) who will answer the following questions:
*# What is the speaker's take-home message (one short sentence)?
*# What are the best points about the presentation's organization, visuals, and delivery? Please give at least 2 specific examples.
*# What points need improvement? Please give at least 2 specific examples.
* We expect that you will take the feedback from your previous presentation into account when doing this presentation.

== Individual Assessment and Reflection ==

Each person on the team will complete an assessment and reflection ''individually''. If you are comfortable with making this assessment publicly available, you may write it up as a wiki page or as a Word document uploaded to your group deliveables page. If you prefer to communicate your assessment privately, then email this to both Drs. Dahlquist and Dionisio.

=== Statement of Work ===

* Describe exactly what you did on the project.
* Provide references or links to artifacts of your work, such as:
** Wiki pages
** Other files or documents
** Code or scripts

=== Assessment of Project ===

* Give an objective assessment of the success of your project workflow and teamwork.
* What worked and what didn't work?
* What would you do differently if you could do it all over again?
* Evaluate your team’s portion of the GRNsight Gene Page Project and Group Report in the following areas:
*# Content: What is the quality of the work?
*# Organization: Comment on the organization of the project and of your group's wiki pages.
*# Completeness: Did your team achieve all of the project objectives? Why or why not?

=== Reflection on the Process ===

* What did you learn?
** With your head (biological or computer science principles)
** With your heart (personal qualities and teamwork qualities that make things work or not work)?
** With your hands (technical skills)?
* What lesson will you take away from this project that you will still use a year from now?

{{Final Project Links}}

[[Category:Group Projects]]

Coder/Designer

2019-11-19T22:28:34Z

Kdahlquist: /* Milestone 4: Design a Database to Store Time-course Microarray Data from four sources */ clarify tasks

{{Final Project Links}}

The Coder/Designer is responsible for creating the Microsoft Access database that will be used by the Data Analyst to prepare an input workbook for GRNmap for the microarray dataset they are analyzing. The Coder/Designer is also the resident expert on the technology being used—assorted software, file management, version control, and troubleshooting. He or she coordinates with Dr. Dahlquist and fellow Coders/Designers in developing the Access database and storing it on Box.

== Guild Members ==

* [[Sulfiknights]]: DeLisa Madere
* [[FunGals]]: Mike Armas
* [[Skinny Genes]]: Mihir

== Milestones ==

The milestones do not necessarily correspond to particular weeks; instead they are sets of tasks grouped together. However, Milestone 3 is a hard prerequisite for proceeding to Milestone 4, so ideally the Coder/Coder guild should finish these milestones (they require some coordination; see below) as soon as possible.

=== Milestone 1: Annotated Bibliography ===

* The Coder/Designer will work with their teams to develop an annotated bibliography of papers relating to their team's assigned paper.

=== Milestone 2: Journal Club Presentation ===

* The Coder/Designer will work with their teams to create and deliver a Journal Club presentation about to their team's assigned paper.

=== Milestone 3: Working Environment Setup ===

Coder/Designer work will require the following software/accounts. The Seaver 120 lab computers are already set up for this; this list is provided for Coders/Designers who need to work on a different computer or outside of the lab.
* Microsoft Access
* Box account (provided by LMU)
** Databases created by the teams will be kept in a [https://lmu.box.com/s/gutpb5qm0a6b2pvjn1j6moqb6y47e903 "BIOL367_Fall2019 > Final Project Database" Box folder].
** Coder/Designer guild members have rights as editor to this folder; all others in the class can only view/download.
** This folder will serve as as the version control mechanism for the Coder/Designer guild.

=== Milestone 4: Design a Database to Store Time-course Microarray Data from four sources ===

* Designer/Coders will work with the QA's to create a MS Access Database to store the yeast time-course microarray data for the dataset being analyzed by the Data Analysts.
* The starting point will be the database already used for the [[Week 10]] assignment, which can be found [https://lmu.box.com/s/kn8l6r639af683ioojqoce5w1g3z7kd2 here] on Box.
** This database is already populated with tables for the Dahlquist Lab microarray data, degradation rates from Neymotin et al. (2014), and initial guesses for production rates.
*** You may need to change the table names of these existing tables so that they make sense with the overall database design.
** You will need to add one or more expression tables for the expression data from your team's article.
*** Work with your team's QA and Data Analysts to determine appropriate column headings for the expression table.
** You will also need to create one or more tables with metadata about the other tables because now the database will contain data from multiple sources, not just one.
*** A major part of the design work will be to figure out what information needs to be in the metadata table so that queries can be easily and uniquely performed on the data.
*** Think about what information would someone need to know to be able to understand how the dataset works. Consult with the QA and Data Analysts to figure out the sample-data relationships and how that should be encoded.

=== Milestone 5: Build an individual database for your team ===

* Once the design work has been completed, you need to actually import the data into the database.
* Initially, each team will have their own database so that the QA and Data analysts can validate and use the database.

=== Milestone 6: Validation and Quality Assurance on Database ===

* The QA will perform quality assurance to make sure that the database is correct and accurate.
** In particular, the QA needs to make sure that all of the rows of data were imported into the database for the expression table(s).
** The QA will make sure that both the ID (SGD systematic name) and Standard Names are included in each expression table and are correct.
* QA's will communicate to the Coder/Designers any changes needed to the database.

=== Milestone 7: Merge completed databases into a single database for the class ===

* As a guild, the Coder/Designers will merge their separate databases into a final product.

{{Final Project Links}}

[[Category:Group Projects]]

Coder/Designer

2019-11-19T22:26:31Z

Kdahlquist: removed reference to github

{{Final Project Links}}

The Coder/Designer is responsible for creating the Microsoft Access database that will be used by the Data Analyst to prepare an input workbook for GRNmap for the microarray dataset they are analyzing. The Coder/Designer is also the resident expert on the technology being used—assorted software, file management, version control, and troubleshooting. He or she coordinates with Dr. Dahlquist and fellow Coders/Designers in developing the Access database and storing it on Box.

== Guild Members ==

* [[Sulfiknights]]: DeLisa Madere
* [[FunGals]]: Mike Armas
* [[Skinny Genes]]: Mihir

== Milestones ==

The milestones do not necessarily correspond to particular weeks; instead they are sets of tasks grouped together. However, Milestone 3 is a hard prerequisite for proceeding to Milestone 4, so ideally the Coder/Coder guild should finish these milestones (they require some coordination; see below) as soon as possible.

=== Milestone 1: Annotated Bibliography ===

* The Coder/Designer will work with their teams to develop an annotated bibliography of papers relating to their team's assigned paper.

=== Milestone 2: Journal Club Presentation ===

* The Coder/Designer will work with their teams to create and deliver a Journal Club presentation about to their team's assigned paper.

=== Milestone 3: Working Environment Setup ===

Coder/Designer work will require the following software/accounts. The Seaver 120 lab computers are already set up for this; this list is provided for Coders/Designers who need to work on a different computer or outside of the lab.
* Microsoft Access
* Box account (provided by LMU)
** Databases created by the teams will be kept in a [https://lmu.box.com/s/gutpb5qm0a6b2pvjn1j6moqb6y47e903 "BIOL367_Fall2019 > Final Project Database" Box folder].
** Coder/Designer guild members have rights as editor to this folder; all others in the class can only view/download.
** This folder will serve as as the version control mechanism for the Coder/Designer guild.

=== Milestone 4: Design a Database to Store Time-course Microarray Data from four sources ===

* Designer/Coders will work with the QA's to create a MS Access Database to store the yeast time-course microarray data for the dataset being analyzed by the Data Analysts.
* The starting point will be the database already used for the [[Week 10]] assignment, which can be found [https://lmu.box.com/s/kn8l6r639af683ioojqoce5w1g3z7kd2 here] on Box.
** This database is already populated with tables for the Dahlquist Lab microarray data, degradation rates from Neymotin et al. (2014), and initial guesses for production rates.
*** You may need to change the table names of these existing tables so that they make sense with the overall database design.
** You will need to add one or more expression tables for the expression data from your team's article.
*** Work with your team's QA and Data Analysts to determine appropriate column headings for the expression table.
** You will also need to create one or more tables with metadata about the other tables because now the database will contain data from multiple sources, not just one.
*** A major part of the design work will be to figure out what information needs to be in the metadata table so that queries can be easily and uniquely performed on the data.

=== Milestone 5: Build an individual database for your team ===

* Once the design work has been completed, you need to actually import the data into the database.
* Initially, each team will have their own database so that the QA and Data analysts can validate and use the database.

=== Milestone 6: Validation and Quality Assurance on Database ===

* The QA will perform quality assurance to make sure that the database is correct and accurate.
** In particular, the QA needs to make sure that all of the rows of data were imported into the database for the expression table(s).
** The QA will make sure that both the ID (SGD systematic name) and Standard Names are included in each expression table and are correct.
* QA's will communicate to the Coder/Designers any changes needed to the database.

=== Milestone 7: Merge completed databases into a single database for the class ===

* As a guild, the Coder/Designers will merge their separate databases into a final product.

{{Final Project Links}}

[[Category:Group Projects]]

Week 12/13

2019-11-19T22:05:06Z

Kdahlquist: /* Overview */ fix link

'''This journal entry is due on Tuesday, November 26, at 12:01 PDT.''' ''(Monday night/Tuesday morning)''

== Overview ==

* Everyone will contribute to [[Week_11#Whole_Team_Journal_Assignment:_Creating_a_Team_Wiki_Page | revising your team's home page.]]
* To make significant progress toward completing the group project [[Final_Project_Deliverables | deliverables]] according to the schedule set by your group.

=== Grading for this assignment ===

* Your individual journal entry for this week is worth a total of 10 points.
* Your team journal entry for this week is worth a total of 3 points.

== Individual Journal Assignment ==

* Store this journal entry as "''username'' Week 12/13" (i.e., this is the text to place between the square brackets when you link to this page).
* Invoke your template on your journal entry page so that you:
** Link from your journal entry page to this Assignment page.
** Link from your journal entry to your user page.
** Add the "Journal Entry" category to the end of your wiki page.
* Because you have invoked your template on your user page, you should also have a:
** Link from your user page to this Assignment page.
** Link to your journal entry from your user page.
* '''''Note that this week, we will add two new categories, "Group Projects" and a category for your team's name.''' Please do not add these to your individual templates because we want these categories to be precisely used for the Group Projects and your team, respectively.''
* Include both the Acknowledgments and References section as specified by the [[Week_1#Academic_Honesty | Week 1]] assignment.
* For your assignment this week document your individual progress in your '''''electronic laboratory notebook'''''.

=== Electronic Lab Notebook ===

Record your progress toward your team's [[Final_Project_Deliverables | deliverables]] for this week in an '''''electronic lab notebook'''''.

== '''Team Journal Assignment''' ==

# Each person needs to write a short executive summary of that person's progress on the project for the week, with links to the relevant individual journal pages (which will have more detailed information).
# Each team member should reflect on the team's progress:
## What worked?
## What didn't work?
## What will I do next to fix what didn't work?

[[Category:Assignment]]
[[Category:Group Projects]]

Week 12/13

2019-11-19T22:04:35Z

Kdahlquist: /* Overview */ paste in revising team page

'''This journal entry is due on Tuesday, November 26, at 12:01 PDT.''' ''(Monday night/Tuesday morning)''

== Overview ==

* Everyone will contribute to [[Week_11#Team_Journal_Assignment | revising your team's home page.]]
* To make significant progress toward completing the group project [[Final_Project_Deliverables | deliverables]] according to the schedule set by your group.

=== Grading for this assignment ===

* Your individual journal entry for this week is worth a total of 10 points.
* Your team journal entry for this week is worth a total of 3 points.

== Individual Journal Assignment ==

* Store this journal entry as "''username'' Week 12/13" (i.e., this is the text to place between the square brackets when you link to this page).
* Invoke your template on your journal entry page so that you:
** Link from your journal entry page to this Assignment page.
** Link from your journal entry to your user page.
** Add the "Journal Entry" category to the end of your wiki page.
* Because you have invoked your template on your user page, you should also have a:
** Link from your user page to this Assignment page.
** Link to your journal entry from your user page.
* '''''Note that this week, we will add two new categories, "Group Projects" and a category for your team's name.''' Please do not add these to your individual templates because we want these categories to be precisely used for the Group Projects and your team, respectively.''
* Include both the Acknowledgments and References section as specified by the [[Week_1#Academic_Honesty | Week 1]] assignment.
* For your assignment this week document your individual progress in your '''''electronic laboratory notebook'''''.

=== Electronic Lab Notebook ===

Record your progress toward your team's [[Final_Project_Deliverables | deliverables]] for this week in an '''''electronic lab notebook'''''.

== '''Team Journal Assignment''' ==

# Each person needs to write a short executive summary of that person's progress on the project for the week, with links to the relevant individual journal pages (which will have more detailed information).
# Each team member should reflect on the team's progress:
## What worked?
## What didn't work?
## What will I do next to fix what didn't work?

[[Category:Assignment]]
[[Category:Group Projects]]

Week 12/13

2019-11-19T22:02:56Z

Kdahlquist: paste in from 2017

'''This journal entry is due on Tuesday, November 26, at 12:01 PDT.''' ''(Monday night/Tuesday morning)''

== Overview ==

The objectives of this week's exercise is to make significant progress toward completing the group project [[Final_Project_Deliverables | deliverables]] according to the schedule set by your group.

=== Grading for this assignment ===

* Your individual journal entry for this week is worth a total of 10 points.
* Your team journal entry for this week is worth a total of 3 points.

== Individual Journal Assignment ==

* Store this journal entry as "''username'' Week 12/13" (i.e., this is the text to place between the square brackets when you link to this page).
* Invoke your template on your journal entry page so that you:
** Link from your journal entry page to this Assignment page.
** Link from your journal entry to your user page.
** Add the "Journal Entry" category to the end of your wiki page.
* Because you have invoked your template on your user page, you should also have a:
** Link from your user page to this Assignment page.
** Link to your journal entry from your user page.
* '''''Note that this week, we will add two new categories, "Group Projects" and a category for your team's name.''' Please do not add these to your individual templates because we want these categories to be precisely used for the Group Projects and your team, respectively.''
* Include both the Acknowledgments and References section as specified by the [[Week_1#Academic_Honesty | Week 1]] assignment.
* For your assignment this week document your individual progress in your '''''electronic laboratory notebook'''''.

=== Electronic Lab Notebook ===

Record your progress toward your team's [[Final_Project_Deliverables | deliverables]] for this week in an '''''electronic lab notebook'''''.

== '''Team Journal Assignment''' ==

# Each person needs to write a short executive summary of that person's progress on the project for the week, with links to the relevant individual journal pages (which will have more detailed information).
# Each team member should reflect on the team's progress:
## What worked?
## What didn't work?
## What will I do next to fix what didn't work?

[[Category:Assignment]]
[[Category:Group Projects]]

Final Project

2019-11-19T22:00:38Z

Kdahlquist: /* Coder/Designer (CD) */ change github to box

{{Final Project Links}}

== Overall Project Goal ==

* To create a Microsoft Access database that contains microarray data for four yeast time-course microarray datasets that can be used to create input Excel workbooks for GRNmap.
* To analyze published microarray data and model candidate gene regulatory networks with GRNmap.
* The project in this class is a pilot project that will be fully implemented in the GRNsight software in the Spring semester.

Each team is responsible for analyzing a different dataset and adding the expression data from that dataset to the final database. The members of each team will also have specific roles, also detailed below. Team members in a specific role are also expected to work with each other across teams as ''guilds'', in order to keep each component of the project coordinated and consistent.

== Group Deliverables ==

* You will give a final group [[Final Project Deliverables#PowerPoint_Presentation | PowerPoint presentation]] in class during the final exam period on '''Tuesday, December 10, 2:00pm'''.
* Final due date for all other deliverables is no later than '''Friday, December 13, 4:00pm''' (according to official final exam schedule for fall, 2019).
* The deliverables should be uploaded and organized onto one group wiki page, or alternately, delivered on digital media to Dr. Dahlquist.
* Each member of the group will be assigned the same grade for the group project.
* Detailed specifications, particularly for the [[Final Project Deliverables#Group Report|group report]] and [[Final Project Deliverables#Individual Assessment and Reflection|individual assessment and reflection]], are given on the [[Final Project Deliverables]] page.
** The Group Report is worth 50 points.
** The Group PowerPoint presentation is worth 50 points.
** All other deliverables listed on the [[Final Project Deliverables | Deliverables]] page, together, are worth 50 points.

=== Individual Deliverable ===

The individual deliverable is an [[Final Project Deliverables#Individual Assessment and Reflection|assessment and reflection]] on the process (either wiki or email, depending on your preference; see note in the linked section):
* Statement of work
* Assessment of the work done
* What was learned

== Team Journal Entries ==

* Each team will write a combined journal entry for each week with contributions from all members.
* [[Week 11]] Creation of page, executive summaries of progress, reflections
* [[Week 12/13]] Revision of page, executive summaries of progress, reflections
* [[Week 15]] Organized deliverables page

The project groups and roles are are:
# Team 1
#* Project Manager: Mike
#* Quality Assurance: Iliana
#* Data Analysis: Emma, Kaitlyn
#* Coder: Mike
# Team 2
#* Project Manager: Naomi
#* Quality Assurance: Naomi, Joey
#* Data Analysis: Marcus, Ivy
#* Designer: DeLisa
# Team 3
#* Project Manager: TBA
#* Quality Assurance: Christina, Jonar
#* Data Analysis: David, Aby
#* Coder: Mihir

== Roles (Guilds) ==

As the project moves forward, we will use class time for team meetings. In addition, we will also have guild meetings where students sharing the same role can work together on common issues. Each student has been assigned a primary role in the project by the instructors (see above).

=== [[Project Manager]] (PM) ===

The project manager makes sure that individuals are fulfilling their roles and performing the tasks on time.

=== [[Quality Assurance]] (QA) ===

The QA team member is the link between the Coder/Designer and the Data Analyst. He or she needs to know the details of the microarray dataset being analyzed so that the database being designed and populated by the Coder/Designer is correct and useful for the Data Analyst. The QA also makes sure that the data analysis steps are being performed correctly and are being correctly documented.

=== [[Data Analysis]] ===

The role of the Data Analyst will be to apply the data analysis pipeline that you learned by analyzing the Dahlquist Lab microarray dataset to complete the analysis of a different published yeast timecourse microarray dataset. The Data Analysts are the end-users of the project, ultimately determining whether the work of the coder/designer and quality assurance members is useful to them.

=== [[Coder/Designer]] (CD) ===

The Coder/Designer is responsible for creating the Microsoft Access database that will be used by the Data Analyst to prepare an input workbook for GRNmap for the microarray dataset they are analyzing. The Coder/Designer is also the resident expert on the technology being used—assorted software, file management, version control, and troubleshooting. He or she coordinates with Dr. Dahlquist and fellow Coders/Designers in developing the Access database and storing it in Box.

== Project Milestones ==

Specific project milestones are found on the individual guild pages.

* [[Project Manager#Milestones|Project Manager]]
* [[Quality Assurance#Milestones|Quality Assurance]]
* [[Data Analysis#Milestones|Data Analysis]]
* [[Coder/Designer#Milestones|Coder]]

{{Final Project Links}}

[[Category:Group Projects]]

Final Project

2019-11-19T22:00:17Z

Kdahlquist: /* Project Milestones */ fix broken link

{{Final Project Links}}

== Overall Project Goal ==

* To create a Microsoft Access database that contains microarray data for four yeast time-course microarray datasets that can be used to create input Excel workbooks for GRNmap.
* To analyze published microarray data and model candidate gene regulatory networks with GRNmap.
* The project in this class is a pilot project that will be fully implemented in the GRNsight software in the Spring semester.

Each team is responsible for analyzing a different dataset and adding the expression data from that dataset to the final database. The members of each team will also have specific roles, also detailed below. Team members in a specific role are also expected to work with each other across teams as ''guilds'', in order to keep each component of the project coordinated and consistent.

== Group Deliverables ==

* You will give a final group [[Final Project Deliverables#PowerPoint_Presentation | PowerPoint presentation]] in class during the final exam period on '''Tuesday, December 10, 2:00pm'''.
* Final due date for all other deliverables is no later than '''Friday, December 13, 4:00pm''' (according to official final exam schedule for fall, 2019).
* The deliverables should be uploaded and organized onto one group wiki page, or alternately, delivered on digital media to Dr. Dahlquist.
* Each member of the group will be assigned the same grade for the group project.
* Detailed specifications, particularly for the [[Final Project Deliverables#Group Report|group report]] and [[Final Project Deliverables#Individual Assessment and Reflection|individual assessment and reflection]], are given on the [[Final Project Deliverables]] page.
** The Group Report is worth 50 points.
** The Group PowerPoint presentation is worth 50 points.
** All other deliverables listed on the [[Final Project Deliverables | Deliverables]] page, together, are worth 50 points.

=== Individual Deliverable ===

The individual deliverable is an [[Final Project Deliverables#Individual Assessment and Reflection|assessment and reflection]] on the process (either wiki or email, depending on your preference; see note in the linked section):
* Statement of work
* Assessment of the work done
* What was learned

== Team Journal Entries ==

* Each team will write a combined journal entry for each week with contributions from all members.
* [[Week 11]] Creation of page, executive summaries of progress, reflections
* [[Week 12/13]] Revision of page, executive summaries of progress, reflections
* [[Week 15]] Organized deliverables page

The project groups and roles are are:
# Team 1
#* Project Manager: Mike
#* Quality Assurance: Iliana
#* Data Analysis: Emma, Kaitlyn
#* Coder: Mike
# Team 2
#* Project Manager: Naomi
#* Quality Assurance: Naomi, Joey
#* Data Analysis: Marcus, Ivy
#* Designer: DeLisa
# Team 3
#* Project Manager: TBA
#* Quality Assurance: Christina, Jonar
#* Data Analysis: David, Aby
#* Coder: Mihir

== Roles (Guilds) ==

As the project moves forward, we will use class time for team meetings. In addition, we will also have guild meetings where students sharing the same role can work together on common issues. Each student has been assigned a primary role in the project by the instructors (see above).

=== [[Project Manager]] (PM) ===

The project manager makes sure that individuals are fulfilling their roles and performing the tasks on time.

=== [[Quality Assurance]] (QA) ===

The QA team member is the link between the Coder/Designer and the Data Analyst. He or she needs to know the details of the microarray dataset being analyzed so that the database being designed and populated by the Coder/Designer is correct and useful for the Data Analyst. The QA also makes sure that the data analysis steps are being performed correctly and are being correctly documented.

=== [[Data Analysis]] ===

The role of the Data Analyst will be to apply the data analysis pipeline that you learned by analyzing the Dahlquist Lab microarray dataset to complete the analysis of a different published yeast timecourse microarray dataset. The Data Analysts are the end-users of the project, ultimately determining whether the work of the coder/designer and quality assurance members is useful to them.

=== [[Coder/Designer]] (CD) ===

The Coder/Designer is responsible for creating the Microsoft Access database that will be used by the Data Analyst to prepare an input workbook for GRNmap for the microarray dataset they are analyzing. The Coder/Designer is also the resident expert on the technology being used—assorted software, file management, version control, and troubleshooting. He or she coordinates with Dr. Dahlquist and fellow Coders/Designers in developing the Access database and storing it in the GitHub code repository.

== Project Milestones ==

Specific project milestones are found on the individual guild pages.

* [[Project Manager#Milestones|Project Manager]]
* [[Quality Assurance#Milestones|Quality Assurance]]
* [[Data Analysis#Milestones|Data Analysis]]
* [[Coder/Designer#Milestones|Coder]]

{{Final Project Links}}

[[Category:Group Projects]]

Final Project Deliverables

2019-11-19T21:58:32Z

Kdahlquist: /* PowerPoint Presentation */ fix syntax

{{ Under Construction }}
{{Final Project Links}}

== Deliverables Checklist ==

# Organized Team deliverables wiki page (or other media (CD or flash drive) with table of contents)
# Group Report (''.doc'', ''.docx'' or ''.pdf'' file)
# Individual statements of work, assessments, reflections (wiki page, ''.doc'', ''.docx'', ''.pdf'', or e-mailed to Dr. Dahlquist)
# Group PowerPoint presentation (given on Tuesday, December 10, ''.ppt'', ''.pptx'' or ''.pdf'' file)
# Excel spreadsheet with ANOVA results/stem formatting (''.xlsx'')
# PowerPoint of ANOVA table, screenshots of stem results (''.pptx''), screenshot of black and white GRNsight input network and colored GRNsight output networks
# Gene List and GO List files from each significant profile (''.txt'' compressed together in a ''.zip'' file)
# YEASTRACT "rank by TF" results (''.xlsx'')
# GRNmap input workbook (with network adjacency matrix, ''.xlsx'')
# GRNmap output workbook (''.xlsx'') and output plots (''.jpg'') zipped together
# MS Access database, unified by the three teams with expression tables and metadata table(s) created (''.accdb'').
# ReadMe for the database that describes the design of the database, references the sources of the data, and has a database schema diagram (''.doc'', ''.docx'', ''.pdf'')
# Query design for populating a GRNmap input workbook from the database (screenshot of MS Access, or SQL code, ''.txt'')
# Electronic notebook corresponding to these the microarray results files ([[Week 12/13]] and [[Week 15]]) to support ''reproducible research'' so that all manipulations of the data and files are documented so that someone else could begin with your starting file, follow the protocol, and obtain your results.

=== Grading ===

* The Group Report is worth 50 points.
* The Group PowerPoint presentation is worth 50 points.
* All other deliverables listed above, together, are worth 50 points.

== Group Report ==

* The report should be written with contributions from all group members.
* Submit as ''.doc'', ''.docx'' or ''.pdf'' file.

=== Style Sheet ===

Use the following guidelines when formatting your report:
* 2.54 cm (1 in) margins on all sides
* Double-spaced
* 12 point Times/Times New Roman font
* Number the pages on the lower-right corner
* Use left justification (“jagged” on the right side)

=== Title Page ===

Include the following information in a standalone title page:
* A descriptive title for your project
** The function of the title is to identify the main result or take-home message of the paper. It should be as specific as possible. It can be a phrase or a sentence. What is the main result of your paper that you want to convey with the title?
* The names of the team members (with middle initials)
* The course number and title of the class
* The date of submission

=== Introduction ===

The introduction gives the background information necessary to understand your report and should be '''2-3''' pages long (double-spaced). The introduction should be in the form of a logical argument that “funnels” from broad to narrow:

<gallery mode="nolines" widths=322px heights=256px>
Funnel.jpg
</gallery>

* States importance of the problem
* States what is known about the problem
** Give an overview of what is known about the regulation of the transcriptional response in yeast from your team's journal article and the annotated bibliography your team developed.
** Discuss how you will approach this by re-analyzing the yeast microarray data from your article.
* States what is unknown about the problem
** Which transcription factors belong in the gene regulatory network that controls the response in yeast is still unknown.
* States clues that suggest how to approach the unknown
** Introduce GRNmap and GRNsight as the answer to this problem.
* States the question the paper is trying to address
** In this case you want to discover new information about the microarray data using GRNmap and GRNsight.
* State the approach
** Give an overview of your team's contribution to GRNsight and why it is needed.

=== Combined Methods/Results/Discussion section ===

This section will summarize the entire workflow and findings for the project with contributions from all team members (Data Analyst, QA, and Coder/Designers). Create a combined flow chart of the tasks/milestones of each of the team members and then briefly describe the flow chart in the text.
* Number each of the figures sequentially and number each of the tables sequentially in order from first mention in the text. You can either embed your figures and tables in the appropriate place in the text or put them all at the end. Do not mix both styles, however.
* Write a descriptive legend for each figure and table that briefly states what the figure/table is and gives a brief key to any labels and abbreviations.

==== Data Analyst ====

* Table of ANOVA results from the [[Week 8]], discussing the interpretation of the p values.
* From the STEM analyis, include as figures the overall results (the screenshot showing all of the clusters) and then focus on the ones you interpreted for the [[Week 10]] journal assignment.
** Include a table showing the GO results for that cluster (just the narrowed down list of terms that you have interpreted).
** Discuss what the p values for the cluster and for the GO term list mean.
** Discuss the biological interpretation of your GO terms.
* Include a table that lists the transcription factors in your final network and their enrichment p value from YEASTRACT.
** Describe how and why you and your partner chose these transcription factors for your network.
** Include a figure (screenshot) of the unweighted networks visualized with GRNsight.
* GRNmap results
** Show the GRNsight visualization (screenshot) of the weighted networks, making sure that the genes are placed in the same relative location as each other an as the unweighted network figure.
** Provide the LSE:minLSE ratio
** Provide a table of the weights, P's, and b's
** Organize and show the individual expression plots
** Interpret the results of the model

==== Coder/Designers ====

* Give a narrative description of the database, including the design of the expression tables and metadata table(s).
* Provide a database schema.

==== QA ====

* Describe the QA process for the database, noting any issues that were encountered, and stating whether data are complete or not.

=== Conclusions ===

* Write a '''1-2''' page conclusion that summarizes the overall project and your findings.
** Overall, what have you learned about the regulation of cold shock in yeast?
** How does the new gene page feature facilitate the data analysis?
** What future directions would you take if you were to continue this project?
** Relate the results of your project to the papers you presented for journal club and included in your bibliography in [[Week 11]].
*** Did you discover anything new that wasn't reported in the journal club paper (Data Analyst/QA)?

=== Acknowledgments ===

Write a short paragraph acknowledging the assistance of anyone who is not a member of your team.

=== References ===

* This section lists all of the references cited in the text of the report (and only those references cited in the paper). Follow the [[Media: BIOL367_Fall2019_GuidelinesforLiteratureCitations.pdf | Guidelines for Literature Citations in a Scientific Paper]] handout for general principles.
* Remember that you need to cite anything for which you are not the original source. Generally, in the introduction, you should aim for a minimum of two in-text citations per paragraph. You may reference the course web site using the appropriate format for a web reference.
* List your references in alphabetical order by first author using the [https://owl.purdue.edu/owl/research_and_citation/apa_style/apa_formatting_and_style_guide/reference_list_basic_rules.html APA style] that you have been using throughout the semester. Note that the proper format must be followed for both articles and websites, just URLs are not sufficient.

== PowerPoint Presentation ==

Each team of students will prepare and give a 20 minute PowerPoint presentation to report the results of their project on Tuesday, December 10 at 2:00-4:00 PM.
* Please follow the [http://brightspace.lmu.edu Presentation Guidelines on Brightspace] for how to format your slides.
* You will need to prepare ~20 slides (assume 1 slide per minute of presentation) and include the following content:
** Title slide that gives the main take-home message as the title of your presentation, the authors, date, and venue (course number and title).
** Outline slide that is a summary of take-home messages of your talk (should mirror your conclusion slide)
** The body of your talk (organized in a logical flow, not necessarily in the order given below).
*** Introduce the importance/significance of the problem and give background on cold shock in yeast. You can draw from the DA/QA's journal club presentation. This should follow the logic of the Introduction section of your group paper.
*** Show a combined flow chart of the tasks/milestones completed by your group (see Materials and Methods of group paper).
*** Show the table of ANOVA results.
*** Show the screenshot of the overall clustering results and the cluster you focused on .
*** Show a table of the GO results from that cluster, giving an interpretation.
*** Show the table of regulatory transcription factors in your network and their p values for enrichment.
*** Show the unweighted and weighted networks in GRNsight (with the genes arranged in the same way in each figure)
*** Database schema diagram for the MS Access database
** Conclusion slide that mirrors your outline
** Future directions
** Acknowledgments
** References
* '''''Your PowerPoint slides must be uploaded to the wiki and linked to from your individual journal page and your team page by 12:01am, Tuesday, December 10.'''''
** You can update your slides before your presentation, but we will be grading the ones you upload by the deadline.
* Your presentation (both the slides and the oral presentation) will be evaluated by the instructors using the [[Presentation Rubric]].
* Your presentation will also be evaluated by your fellow classmates (anonymously) who will answer the following questions:
*# What is the speaker's take-home message (one short sentence)?
*# What are the best points about the presentation's organization, visuals, and delivery? Please give at least 2 specific examples.
*# What points need improvement? Please give at least 2 specific examples.
* We expect that you will take the feedback from your previous presentation into account when doing this presentation.

== Individual Assessment and Reflection ==

Each person on the team will complete an assessment and reflection ''individually''. If you are comfortable with making this assessment publicly available, you may write it up as a wiki page or as a Word document uploaded to your group deliveables page. If you prefer to communicate your assessment privately, then email this to both Drs. Dahlquist and Dionisio.

=== Statement of Work ===

* Describe exactly what you did on the project.
* Provide references or links to artifacts of your work, such as:
** Wiki pages
** Other files or documents
** Code or scripts

=== Assessment of Project ===

* Give an objective assessment of the success of your project workflow and teamwork.
* What worked and what didn't work?
* What would you do differently if you could do it all over again?
* Evaluate your team’s portion of the GRNsight Gene Page Project and Group Report in the following areas:
*# Content: What is the quality of the work?
*# Organization: Comment on the organization of the project and of your group's wiki pages.
*# Completeness: Did your team achieve all of the project objectives? Why or why not?

=== Reflection on the Process ===

* What did you learn?
** With your head (biological or computer science principles)
** With your heart (personal qualities and teamwork qualities that make things work or not work)?
** With your hands (technical skills)?
* What lesson will you take away from this project that you will still use a year from now?

{{Final Project Links}}

[[Category:Group Projects]]

Final Project Deliverables

2019-11-19T21:58:18Z

Kdahlquist: /* QA */ clarify

{{ Under Construction }}
{{Final Project Links}}

== Deliverables Checklist ==

# Organized Team deliverables wiki page (or other media (CD or flash drive) with table of contents)
# Group Report (''.doc'', ''.docx'' or ''.pdf'' file)
# Individual statements of work, assessments, reflections (wiki page, ''.doc'', ''.docx'', ''.pdf'', or e-mailed to Dr. Dahlquist)
# Group PowerPoint presentation (given on Tuesday, December 10, ''.ppt'', ''.pptx'' or ''.pdf'' file)
# Excel spreadsheet with ANOVA results/stem formatting (''.xlsx'')
# PowerPoint of ANOVA table, screenshots of stem results (''.pptx''), screenshot of black and white GRNsight input network and colored GRNsight output networks
# Gene List and GO List files from each significant profile (''.txt'' compressed together in a ''.zip'' file)
# YEASTRACT "rank by TF" results (''.xlsx'')
# GRNmap input workbook (with network adjacency matrix, ''.xlsx'')
# GRNmap output workbook (''.xlsx'') and output plots (''.jpg'') zipped together
# MS Access database, unified by the three teams with expression tables and metadata table(s) created (''.accdb'').
# ReadMe for the database that describes the design of the database, references the sources of the data, and has a database schema diagram (''.doc'', ''.docx'', ''.pdf'')
# Query design for populating a GRNmap input workbook from the database (screenshot of MS Access, or SQL code, ''.txt'')
# Electronic notebook corresponding to these the microarray results files ([[Week 12/13]] and [[Week 15]]) to support ''reproducible research'' so that all manipulations of the data and files are documented so that someone else could begin with your starting file, follow the protocol, and obtain your results.

=== Grading ===

* The Group Report is worth 50 points.
* The Group PowerPoint presentation is worth 50 points.
* All other deliverables listed above, together, are worth 50 points.

== Group Report ==

* The report should be written with contributions from all group members.
* Submit as ''.doc'', ''.docx'' or ''.pdf'' file.

=== Style Sheet ===

Use the following guidelines when formatting your report:
* 2.54 cm (1 in) margins on all sides
* Double-spaced
* 12 point Times/Times New Roman font
* Number the pages on the lower-right corner
* Use left justification (“jagged” on the right side)

=== Title Page ===

Include the following information in a standalone title page:
* A descriptive title for your project
** The function of the title is to identify the main result or take-home message of the paper. It should be as specific as possible. It can be a phrase or a sentence. What is the main result of your paper that you want to convey with the title?
* The names of the team members (with middle initials)
* The course number and title of the class
* The date of submission

=== Introduction ===

The introduction gives the background information necessary to understand your report and should be '''2-3''' pages long (double-spaced). The introduction should be in the form of a logical argument that “funnels” from broad to narrow:

<gallery mode="nolines" widths=322px heights=256px>
Funnel.jpg
</gallery>

* States importance of the problem
* States what is known about the problem
** Give an overview of what is known about the regulation of the transcriptional response in yeast from your team's journal article and the annotated bibliography your team developed.
** Discuss how you will approach this by re-analyzing the yeast microarray data from your article.
* States what is unknown about the problem
** Which transcription factors belong in the gene regulatory network that controls the response in yeast is still unknown.
* States clues that suggest how to approach the unknown
** Introduce GRNmap and GRNsight as the answer to this problem.
* States the question the paper is trying to address
** In this case you want to discover new information about the microarray data using GRNmap and GRNsight.
* State the approach
** Give an overview of your team's contribution to GRNsight and why it is needed.

=== Combined Methods/Results/Discussion section ===

This section will summarize the entire workflow and findings for the project with contributions from all team members (Data Analyst, QA, and Coder/Designers). Create a combined flow chart of the tasks/milestones of each of the team members and then briefly describe the flow chart in the text.
* Number each of the figures sequentially and number each of the tables sequentially in order from first mention in the text. You can either embed your figures and tables in the appropriate place in the text or put them all at the end. Do not mix both styles, however.
* Write a descriptive legend for each figure and table that briefly states what the figure/table is and gives a brief key to any labels and abbreviations.

==== Data Analyst ====

* Table of ANOVA results from the [[Week 8]], discussing the interpretation of the p values.
* From the STEM analyis, include as figures the overall results (the screenshot showing all of the clusters) and then focus on the ones you interpreted for the [[Week 10]] journal assignment.
** Include a table showing the GO results for that cluster (just the narrowed down list of terms that you have interpreted).
** Discuss what the p values for the cluster and for the GO term list mean.
** Discuss the biological interpretation of your GO terms.
* Include a table that lists the transcription factors in your final network and their enrichment p value from YEASTRACT.
** Describe how and why you and your partner chose these transcription factors for your network.
** Include a figure (screenshot) of the unweighted networks visualized with GRNsight.
* GRNmap results
** Show the GRNsight visualization (screenshot) of the weighted networks, making sure that the genes are placed in the same relative location as each other an as the unweighted network figure.
** Provide the LSE:minLSE ratio
** Provide a table of the weights, P's, and b's
** Organize and show the individual expression plots
** Interpret the results of the model

==== Coder/Designers ====

* Give a narrative description of the database, including the design of the expression tables and metadata table(s).
* Provide a database schema.

==== QA ====

* Describe the QA process for the database, noting any issues that were encountered, and stating whether data are complete or not.

=== Conclusions ===

* Write a '''1-2''' page conclusion that summarizes the overall project and your findings.
** Overall, what have you learned about the regulation of cold shock in yeast?
** How does the new gene page feature facilitate the data analysis?
** What future directions would you take if you were to continue this project?
** Relate the results of your project to the papers you presented for journal club and included in your bibliography in [[Week 11]].
*** Did you discover anything new that wasn't reported in the journal club paper (Data Analyst/QA)?

=== Acknowledgments ===

Write a short paragraph acknowledging the assistance of anyone who is not a member of your team.

=== References ===

* This section lists all of the references cited in the text of the report (and only those references cited in the paper). Follow the [[Media: BIOL367_Fall2019_GuidelinesforLiteratureCitations.pdf | Guidelines for Literature Citations in a Scientific Paper]] handout for general principles.
* Remember that you need to cite anything for which you are not the original source. Generally, in the introduction, you should aim for a minimum of two in-text citations per paragraph. You may reference the course web site using the appropriate format for a web reference.
* List your references in alphabetical order by first author using the [https://owl.purdue.edu/owl/research_and_citation/apa_style/apa_formatting_and_style_guide/reference_list_basic_rules.html APA style] that you have been using throughout the semester. Note that the proper format must be followed for both articles and websites, just URLs are not sufficient.

== PowerPoint Presentation ==

Each team of students will prepare and give a 20 minute PowerPoint presentation to report the results of their project on Tuesday, December 10 at 2:00-4:00 PM.
* Please follow the [http://brightspace.lmu.edu Presentation Guidelines on Brightspace]] for how to format your slides.
* You will need to prepare ~20 slides (assume 1 slide per minute of presentation) and include the following content:
** Title slide that gives the main take-home message as the title of your presentation, the authors, date, and venue (course number and title).
** Outline slide that is a summary of take-home messages of your talk (should mirror your conclusion slide)
** The body of your talk (organized in a logical flow, not necessarily in the order given below).
*** Introduce the importance/significance of the problem and give background on cold shock in yeast. You can draw from the DA/QA's journal club presentation. This should follow the logic of the Introduction section of your group paper.
*** Show a combined flow chart of the tasks/milestones completed by your group (see Materials and Methods of group paper).
*** Show the table of ANOVA results.
*** Show the screenshot of the overall clustering results and the cluster you focused on .
*** Show a table of the GO results from that cluster, giving an interpretation.
*** Show the table of regulatory transcription factors in your network and their p values for enrichment.
*** Show the unweighted and weighted networks in GRNsight (with the genes arranged in the same way in each figure)
*** Database schema diagram for the MS Access database
** Conclusion slide that mirrors your outline
** Future directions
** Acknowledgments
** References
* '''''Your PowerPoint slides must be uploaded to the wiki and linked to from your individual journal page and your team page by 12:01am, Tuesday, December 10.'''''
** You can update your slides before your presentation, but we will be grading the ones you upload by the deadline.
* Your presentation (both the slides and the oral presentation) will be evaluated by the instructors using the [[Presentation Rubric]].
* Your presentation will also be evaluated by your fellow classmates (anonymously) who will answer the following questions:
*# What is the speaker's take-home message (one short sentence)?
*# What are the best points about the presentation's organization, visuals, and delivery? Please give at least 2 specific examples.
*# What points need improvement? Please give at least 2 specific examples.
* We expect that you will take the feedback from your previous presentation into account when doing this presentation.

== Individual Assessment and Reflection ==

Each person on the team will complete an assessment and reflection ''individually''. If you are comfortable with making this assessment publicly available, you may write it up as a wiki page or as a Word document uploaded to your group deliveables page. If you prefer to communicate your assessment privately, then email this to both Drs. Dahlquist and Dionisio.

=== Statement of Work ===

* Describe exactly what you did on the project.
* Provide references or links to artifacts of your work, such as:
** Wiki pages
** Other files or documents
** Code or scripts

=== Assessment of Project ===

* Give an objective assessment of the success of your project workflow and teamwork.
* What worked and what didn't work?
* What would you do differently if you could do it all over again?
* Evaluate your team’s portion of the GRNsight Gene Page Project and Group Report in the following areas:
*# Content: What is the quality of the work?
*# Organization: Comment on the organization of the project and of your group's wiki pages.
*# Completeness: Did your team achieve all of the project objectives? Why or why not?

=== Reflection on the Process ===

* What did you learn?
** With your head (biological or computer science principles)
** With your heart (personal qualities and teamwork qualities that make things work or not work)?
** With your hands (technical skills)?
* What lesson will you take away from this project that you will still use a year from now?

{{Final Project Links}}

[[Category:Group Projects]]

Final Project Deliverables

2019-11-19T21:57:36Z

Kdahlquist: /* QA */ modify

{{ Under Construction }}
{{Final Project Links}}

== Deliverables Checklist ==

# Organized Team deliverables wiki page (or other media (CD or flash drive) with table of contents)
# Group Report (''.doc'', ''.docx'' or ''.pdf'' file)
# Individual statements of work, assessments, reflections (wiki page, ''.doc'', ''.docx'', ''.pdf'', or e-mailed to Dr. Dahlquist)
# Group PowerPoint presentation (given on Tuesday, December 10, ''.ppt'', ''.pptx'' or ''.pdf'' file)
# Excel spreadsheet with ANOVA results/stem formatting (''.xlsx'')
# PowerPoint of ANOVA table, screenshots of stem results (''.pptx''), screenshot of black and white GRNsight input network and colored GRNsight output networks
# Gene List and GO List files from each significant profile (''.txt'' compressed together in a ''.zip'' file)
# YEASTRACT "rank by TF" results (''.xlsx'')
# GRNmap input workbook (with network adjacency matrix, ''.xlsx'')
# GRNmap output workbook (''.xlsx'') and output plots (''.jpg'') zipped together
# MS Access database, unified by the three teams with expression tables and metadata table(s) created (''.accdb'').
# ReadMe for the database that describes the design of the database, references the sources of the data, and has a database schema diagram (''.doc'', ''.docx'', ''.pdf'')
# Query design for populating a GRNmap input workbook from the database (screenshot of MS Access, or SQL code, ''.txt'')
# Electronic notebook corresponding to these the microarray results files ([[Week 12/13]] and [[Week 15]]) to support ''reproducible research'' so that all manipulations of the data and files are documented so that someone else could begin with your starting file, follow the protocol, and obtain your results.

=== Grading ===

* The Group Report is worth 50 points.
* The Group PowerPoint presentation is worth 50 points.
* All other deliverables listed above, together, are worth 50 points.

== Group Report ==

* The report should be written with contributions from all group members.
* Submit as ''.doc'', ''.docx'' or ''.pdf'' file.

=== Style Sheet ===

Use the following guidelines when formatting your report:
* 2.54 cm (1 in) margins on all sides
* Double-spaced
* 12 point Times/Times New Roman font
* Number the pages on the lower-right corner
* Use left justification (“jagged” on the right side)

=== Title Page ===

Include the following information in a standalone title page:
* A descriptive title for your project
** The function of the title is to identify the main result or take-home message of the paper. It should be as specific as possible. It can be a phrase or a sentence. What is the main result of your paper that you want to convey with the title?
* The names of the team members (with middle initials)
* The course number and title of the class
* The date of submission

=== Introduction ===

The introduction gives the background information necessary to understand your report and should be '''2-3''' pages long (double-spaced). The introduction should be in the form of a logical argument that “funnels” from broad to narrow:

<gallery mode="nolines" widths=322px heights=256px>
Funnel.jpg
</gallery>

* States importance of the problem
* States what is known about the problem
** Give an overview of what is known about the regulation of the transcriptional response in yeast from your team's journal article and the annotated bibliography your team developed.
** Discuss how you will approach this by re-analyzing the yeast microarray data from your article.
* States what is unknown about the problem
** Which transcription factors belong in the gene regulatory network that controls the response in yeast is still unknown.
* States clues that suggest how to approach the unknown
** Introduce GRNmap and GRNsight as the answer to this problem.
* States the question the paper is trying to address
** In this case you want to discover new information about the microarray data using GRNmap and GRNsight.
* State the approach
** Give an overview of your team's contribution to GRNsight and why it is needed.

=== Combined Methods/Results/Discussion section ===

This section will summarize the entire workflow and findings for the project with contributions from all team members (Data Analyst, QA, and Coder/Designers). Create a combined flow chart of the tasks/milestones of each of the team members and then briefly describe the flow chart in the text.
* Number each of the figures sequentially and number each of the tables sequentially in order from first mention in the text. You can either embed your figures and tables in the appropriate place in the text or put them all at the end. Do not mix both styles, however.
* Write a descriptive legend for each figure and table that briefly states what the figure/table is and gives a brief key to any labels and abbreviations.

==== Data Analyst ====

* Table of ANOVA results from the [[Week 8]], discussing the interpretation of the p values.
* From the STEM analyis, include as figures the overall results (the screenshot showing all of the clusters) and then focus on the ones you interpreted for the [[Week 10]] journal assignment.
** Include a table showing the GO results for that cluster (just the narrowed down list of terms that you have interpreted).
** Discuss what the p values for the cluster and for the GO term list mean.
** Discuss the biological interpretation of your GO terms.
* Include a table that lists the transcription factors in your final network and their enrichment p value from YEASTRACT.
** Describe how and why you and your partner chose these transcription factors for your network.
** Include a figure (screenshot) of the unweighted networks visualized with GRNsight.
* GRNmap results
** Show the GRNsight visualization (screenshot) of the weighted networks, making sure that the genes are placed in the same relative location as each other an as the unweighted network figure.
** Provide the LSE:minLSE ratio
** Provide a table of the weights, P's, and b's
** Organize and show the individual expression plots
** Interpret the results of the model

==== Coder/Designers ====

* Give a narrative description of the database, including the design of the expression tables and metadata table(s).
* Provide a database schema.

==== QA ====

* Describe the QA process for the database.

=== Conclusions ===

* Write a '''1-2''' page conclusion that summarizes the overall project and your findings.
** Overall, what have you learned about the regulation of cold shock in yeast?
** How does the new gene page feature facilitate the data analysis?
** What future directions would you take if you were to continue this project?
** Relate the results of your project to the papers you presented for journal club and included in your bibliography in [[Week 11]].
*** Did you discover anything new that wasn't reported in the journal club paper (Data Analyst/QA)?

=== Acknowledgments ===

Write a short paragraph acknowledging the assistance of anyone who is not a member of your team.

=== References ===

* This section lists all of the references cited in the text of the report (and only those references cited in the paper). Follow the [[Media: BIOL367_Fall2019_GuidelinesforLiteratureCitations.pdf | Guidelines for Literature Citations in a Scientific Paper]] handout for general principles.
* Remember that you need to cite anything for which you are not the original source. Generally, in the introduction, you should aim for a minimum of two in-text citations per paragraph. You may reference the course web site using the appropriate format for a web reference.
* List your references in alphabetical order by first author using the [https://owl.purdue.edu/owl/research_and_citation/apa_style/apa_formatting_and_style_guide/reference_list_basic_rules.html APA style] that you have been using throughout the semester. Note that the proper format must be followed for both articles and websites, just URLs are not sufficient.

== PowerPoint Presentation ==

Each team of students will prepare and give a 20 minute PowerPoint presentation to report the results of their project on Tuesday, December 10 at 2:00-4:00 PM.
* Please follow the [http://brightspace.lmu.edu Presentation Guidelines on Brightspace]] for how to format your slides.
* You will need to prepare ~20 slides (assume 1 slide per minute of presentation) and include the following content:
** Title slide that gives the main take-home message as the title of your presentation, the authors, date, and venue (course number and title).
** Outline slide that is a summary of take-home messages of your talk (should mirror your conclusion slide)
** The body of your talk (organized in a logical flow, not necessarily in the order given below).
*** Introduce the importance/significance of the problem and give background on cold shock in yeast. You can draw from the DA/QA's journal club presentation. This should follow the logic of the Introduction section of your group paper.
*** Show a combined flow chart of the tasks/milestones completed by your group (see Materials and Methods of group paper).
*** Show the table of ANOVA results.
*** Show the screenshot of the overall clustering results and the cluster you focused on .
*** Show a table of the GO results from that cluster, giving an interpretation.
*** Show the table of regulatory transcription factors in your network and their p values for enrichment.
*** Show the unweighted and weighted networks in GRNsight (with the genes arranged in the same way in each figure)
*** Database schema diagram for the MS Access database
** Conclusion slide that mirrors your outline
** Future directions
** Acknowledgments
** References
* '''''Your PowerPoint slides must be uploaded to the wiki and linked to from your individual journal page and your team page by 12:01am, Tuesday, December 10.'''''
** You can update your slides before your presentation, but we will be grading the ones you upload by the deadline.
* Your presentation (both the slides and the oral presentation) will be evaluated by the instructors using the [[Presentation Rubric]].
* Your presentation will also be evaluated by your fellow classmates (anonymously) who will answer the following questions:
*# What is the speaker's take-home message (one short sentence)?
*# What are the best points about the presentation's organization, visuals, and delivery? Please give at least 2 specific examples.
*# What points need improvement? Please give at least 2 specific examples.
* We expect that you will take the feedback from your previous presentation into account when doing this presentation.

== Individual Assessment and Reflection ==

Each person on the team will complete an assessment and reflection ''individually''. If you are comfortable with making this assessment publicly available, you may write it up as a wiki page or as a Word document uploaded to your group deliveables page. If you prefer to communicate your assessment privately, then email this to both Drs. Dahlquist and Dionisio.

=== Statement of Work ===

* Describe exactly what you did on the project.
* Provide references or links to artifacts of your work, such as:
** Wiki pages
** Other files or documents
** Code or scripts

=== Assessment of Project ===

* Give an objective assessment of the success of your project workflow and teamwork.
* What worked and what didn't work?
* What would you do differently if you could do it all over again?
* Evaluate your team’s portion of the GRNsight Gene Page Project and Group Report in the following areas:
*# Content: What is the quality of the work?
*# Organization: Comment on the organization of the project and of your group's wiki pages.
*# Completeness: Did your team achieve all of the project objectives? Why or why not?

=== Reflection on the Process ===

* What did you learn?
** With your head (biological or computer science principles)
** With your heart (personal qualities and teamwork qualities that make things work or not work)?
** With your hands (technical skills)?
* What lesson will you take away from this project that you will still use a year from now?

{{Final Project Links}}

[[Category:Group Projects]]

Final Project Deliverables

2019-11-19T21:57:02Z

Kdahlquist: /* Coders */ modify deliverables

{{ Under Construction }}
{{Final Project Links}}

== Deliverables Checklist ==

# Organized Team deliverables wiki page (or other media (CD or flash drive) with table of contents)
# Group Report (''.doc'', ''.docx'' or ''.pdf'' file)
# Individual statements of work, assessments, reflections (wiki page, ''.doc'', ''.docx'', ''.pdf'', or e-mailed to Dr. Dahlquist)
# Group PowerPoint presentation (given on Tuesday, December 10, ''.ppt'', ''.pptx'' or ''.pdf'' file)
# Excel spreadsheet with ANOVA results/stem formatting (''.xlsx'')
# PowerPoint of ANOVA table, screenshots of stem results (''.pptx''), screenshot of black and white GRNsight input network and colored GRNsight output networks
# Gene List and GO List files from each significant profile (''.txt'' compressed together in a ''.zip'' file)
# YEASTRACT "rank by TF" results (''.xlsx'')
# GRNmap input workbook (with network adjacency matrix, ''.xlsx'')
# GRNmap output workbook (''.xlsx'') and output plots (''.jpg'') zipped together
# MS Access database, unified by the three teams with expression tables and metadata table(s) created (''.accdb'').
# ReadMe for the database that describes the design of the database, references the sources of the data, and has a database schema diagram (''.doc'', ''.docx'', ''.pdf'')
# Query design for populating a GRNmap input workbook from the database (screenshot of MS Access, or SQL code, ''.txt'')
# Electronic notebook corresponding to these the microarray results files ([[Week 12/13]] and [[Week 15]]) to support ''reproducible research'' so that all manipulations of the data and files are documented so that someone else could begin with your starting file, follow the protocol, and obtain your results.

=== Grading ===

* The Group Report is worth 50 points.
* The Group PowerPoint presentation is worth 50 points.
* All other deliverables listed above, together, are worth 50 points.

== Group Report ==

* The report should be written with contributions from all group members.
* Submit as ''.doc'', ''.docx'' or ''.pdf'' file.

=== Style Sheet ===

Use the following guidelines when formatting your report:
* 2.54 cm (1 in) margins on all sides
* Double-spaced
* 12 point Times/Times New Roman font
* Number the pages on the lower-right corner
* Use left justification (“jagged” on the right side)

=== Title Page ===

Include the following information in a standalone title page:
* A descriptive title for your project
** The function of the title is to identify the main result or take-home message of the paper. It should be as specific as possible. It can be a phrase or a sentence. What is the main result of your paper that you want to convey with the title?
* The names of the team members (with middle initials)
* The course number and title of the class
* The date of submission

=== Introduction ===

The introduction gives the background information necessary to understand your report and should be '''2-3''' pages long (double-spaced). The introduction should be in the form of a logical argument that “funnels” from broad to narrow:

<gallery mode="nolines" widths=322px heights=256px>
Funnel.jpg
</gallery>

* States importance of the problem
* States what is known about the problem
** Give an overview of what is known about the regulation of the transcriptional response in yeast from your team's journal article and the annotated bibliography your team developed.
** Discuss how you will approach this by re-analyzing the yeast microarray data from your article.
* States what is unknown about the problem
** Which transcription factors belong in the gene regulatory network that controls the response in yeast is still unknown.
* States clues that suggest how to approach the unknown
** Introduce GRNmap and GRNsight as the answer to this problem.
* States the question the paper is trying to address
** In this case you want to discover new information about the microarray data using GRNmap and GRNsight.
* State the approach
** Give an overview of your team's contribution to GRNsight and why it is needed.

=== Combined Methods/Results/Discussion section ===

This section will summarize the entire workflow and findings for the project with contributions from all team members (Data Analyst, QA, and Coder/Designers). Create a combined flow chart of the tasks/milestones of each of the team members and then briefly describe the flow chart in the text.
* Number each of the figures sequentially and number each of the tables sequentially in order from first mention in the text. You can either embed your figures and tables in the appropriate place in the text or put them all at the end. Do not mix both styles, however.
* Write a descriptive legend for each figure and table that briefly states what the figure/table is and gives a brief key to any labels and abbreviations.

==== Data Analyst ====

* Table of ANOVA results from the [[Week 8]], discussing the interpretation of the p values.
* From the STEM analyis, include as figures the overall results (the screenshot showing all of the clusters) and then focus on the ones you interpreted for the [[Week 10]] journal assignment.
** Include a table showing the GO results for that cluster (just the narrowed down list of terms that you have interpreted).
** Discuss what the p values for the cluster and for the GO term list mean.
** Discuss the biological interpretation of your GO terms.
* Include a table that lists the transcription factors in your final network and their enrichment p value from YEASTRACT.
** Describe how and why you and your partner chose these transcription factors for your network.
** Include a figure (screenshot) of the unweighted networks visualized with GRNsight.
* GRNmap results
** Show the GRNsight visualization (screenshot) of the weighted networks, making sure that the genes are placed in the same relative location as each other an as the unweighted network figure.
** Provide the LSE:minLSE ratio
** Provide a table of the weights, P's, and b's
** Organize and show the individual expression plots
** Interpret the results of the model

==== Coder/Designers ====

* Give a narrative description of the database, including the design of the expression tables and metadata table(s).
* Provide a database schema.

==== QA ====

* Describe the data selected from each database for the gene page and the rationale for its inclusion.
* Describe any other tasks undertaken by the QA.

=== Conclusions ===

* Write a '''1-2''' page conclusion that summarizes the overall project and your findings.
** Overall, what have you learned about the regulation of cold shock in yeast?
** How does the new gene page feature facilitate the data analysis?
** What future directions would you take if you were to continue this project?
** Relate the results of your project to the papers you presented for journal club and included in your bibliography in [[Week 11]].
*** Did you discover anything new that wasn't reported in the journal club paper (Data Analyst/QA)?

=== Acknowledgments ===

Write a short paragraph acknowledging the assistance of anyone who is not a member of your team.

=== References ===

* This section lists all of the references cited in the text of the report (and only those references cited in the paper). Follow the [[Media: BIOL367_Fall2019_GuidelinesforLiteratureCitations.pdf | Guidelines for Literature Citations in a Scientific Paper]] handout for general principles.
* Remember that you need to cite anything for which you are not the original source. Generally, in the introduction, you should aim for a minimum of two in-text citations per paragraph. You may reference the course web site using the appropriate format for a web reference.
* List your references in alphabetical order by first author using the [https://owl.purdue.edu/owl/research_and_citation/apa_style/apa_formatting_and_style_guide/reference_list_basic_rules.html APA style] that you have been using throughout the semester. Note that the proper format must be followed for both articles and websites, just URLs are not sufficient.

== PowerPoint Presentation ==

Each team of students will prepare and give a 20 minute PowerPoint presentation to report the results of their project on Tuesday, December 10 at 2:00-4:00 PM.
* Please follow the [http://brightspace.lmu.edu Presentation Guidelines on Brightspace]] for how to format your slides.
* You will need to prepare ~20 slides (assume 1 slide per minute of presentation) and include the following content:
** Title slide that gives the main take-home message as the title of your presentation, the authors, date, and venue (course number and title).
** Outline slide that is a summary of take-home messages of your talk (should mirror your conclusion slide)
** The body of your talk (organized in a logical flow, not necessarily in the order given below).
*** Introduce the importance/significance of the problem and give background on cold shock in yeast. You can draw from the DA/QA's journal club presentation. This should follow the logic of the Introduction section of your group paper.
*** Show a combined flow chart of the tasks/milestones completed by your group (see Materials and Methods of group paper).
*** Show the table of ANOVA results.
*** Show the screenshot of the overall clustering results and the cluster you focused on .
*** Show a table of the GO results from that cluster, giving an interpretation.
*** Show the table of regulatory transcription factors in your network and their p values for enrichment.
*** Show the unweighted and weighted networks in GRNsight (with the genes arranged in the same way in each figure)
*** Database schema diagram for the MS Access database
** Conclusion slide that mirrors your outline
** Future directions
** Acknowledgments
** References
* '''''Your PowerPoint slides must be uploaded to the wiki and linked to from your individual journal page and your team page by 12:01am, Tuesday, December 10.'''''
** You can update your slides before your presentation, but we will be grading the ones you upload by the deadline.
* Your presentation (both the slides and the oral presentation) will be evaluated by the instructors using the [[Presentation Rubric]].
* Your presentation will also be evaluated by your fellow classmates (anonymously) who will answer the following questions:
*# What is the speaker's take-home message (one short sentence)?
*# What are the best points about the presentation's organization, visuals, and delivery? Please give at least 2 specific examples.
*# What points need improvement? Please give at least 2 specific examples.
* We expect that you will take the feedback from your previous presentation into account when doing this presentation.

== Individual Assessment and Reflection ==

Each person on the team will complete an assessment and reflection ''individually''. If you are comfortable with making this assessment publicly available, you may write it up as a wiki page or as a Word document uploaded to your group deliveables page. If you prefer to communicate your assessment privately, then email this to both Drs. Dahlquist and Dionisio.

=== Statement of Work ===

* Describe exactly what you did on the project.
* Provide references or links to artifacts of your work, such as:
** Wiki pages
** Other files or documents
** Code or scripts

=== Assessment of Project ===

* Give an objective assessment of the success of your project workflow and teamwork.
* What worked and what didn't work?
* What would you do differently if you could do it all over again?
* Evaluate your team’s portion of the GRNsight Gene Page Project and Group Report in the following areas:
*# Content: What is the quality of the work?
*# Organization: Comment on the organization of the project and of your group's wiki pages.
*# Completeness: Did your team achieve all of the project objectives? Why or why not?

=== Reflection on the Process ===

* What did you learn?
** With your head (biological or computer science principles)
** With your heart (personal qualities and teamwork qualities that make things work or not work)?
** With your hands (technical skills)?
* What lesson will you take away from this project that you will still use a year from now?

{{Final Project Links}}

[[Category:Group Projects]]

Final Project Deliverables

2019-11-19T21:45:49Z

Kdahlquist: /* Data Analyst */ fill in details about results

{{ Under Construction }}
{{Final Project Links}}

== Deliverables Checklist ==

# Organized Team deliverables wiki page (or other media (CD or flash drive) with table of contents)
# Group Report (''.doc'', ''.docx'' or ''.pdf'' file)
# Individual statements of work, assessments, reflections (wiki page, ''.doc'', ''.docx'', ''.pdf'', or e-mailed to Dr. Dahlquist)
# Group PowerPoint presentation (given on Tuesday, December 10, ''.ppt'', ''.pptx'' or ''.pdf'' file)
# Excel spreadsheet with ANOVA results/stem formatting (''.xlsx'')
# PowerPoint of ANOVA table, screenshots of stem results (''.pptx''), screenshot of black and white GRNsight input network and colored GRNsight output networks
# Gene List and GO List files from each significant profile (''.txt'' compressed together in a ''.zip'' file)
# YEASTRACT "rank by TF" results (''.xlsx'')
# GRNmap input workbook (with network adjacency matrix, ''.xlsx'')
# GRNmap output workbook (''.xlsx'') and output plots (''.jpg'') zipped together
# MS Access database, unified by the three teams with expression tables and metadata table(s) created (''.accdb'').
# ReadMe for the database that describes the design of the database, references the sources of the data, and has a database schema diagram (''.doc'', ''.docx'', ''.pdf'')
# Query design for populating a GRNmap input workbook from the database (screenshot of MS Access, or SQL code, ''.txt'')
# Electronic notebook corresponding to these the microarray results files ([[Week 12/13]] and [[Week 15]]) to support ''reproducible research'' so that all manipulations of the data and files are documented so that someone else could begin with your starting file, follow the protocol, and obtain your results.

=== Grading ===

* The Group Report is worth 50 points.
* The Group PowerPoint presentation is worth 50 points.
* All other deliverables listed above, together, are worth 50 points.

== Group Report ==

* The report should be written with contributions from all group members.
* Submit as ''.doc'', ''.docx'' or ''.pdf'' file.

=== Style Sheet ===

Use the following guidelines when formatting your report:
* 2.54 cm (1 in) margins on all sides
* Double-spaced
* 12 point Times/Times New Roman font
* Number the pages on the lower-right corner
* Use left justification (“jagged” on the right side)

=== Title Page ===

Include the following information in a standalone title page:
* A descriptive title for your project
** The function of the title is to identify the main result or take-home message of the paper. It should be as specific as possible. It can be a phrase or a sentence. What is the main result of your paper that you want to convey with the title?
* The names of the team members (with middle initials)
* The course number and title of the class
* The date of submission

=== Introduction ===

The introduction gives the background information necessary to understand your report and should be '''2-3''' pages long (double-spaced). The introduction should be in the form of a logical argument that “funnels” from broad to narrow:

<gallery mode="nolines" widths=322px heights=256px>
Funnel.jpg
</gallery>

* States importance of the problem
* States what is known about the problem
** Give an overview of what is known about the regulation of the transcriptional response in yeast from your team's journal article and the annotated bibliography your team developed.
** Discuss how you will approach this by re-analyzing the yeast microarray data from your article.
* States what is unknown about the problem
** Which transcription factors belong in the gene regulatory network that controls the response in yeast is still unknown.
* States clues that suggest how to approach the unknown
** Introduce GRNmap and GRNsight as the answer to this problem.
* States the question the paper is trying to address
** In this case you want to discover new information about the microarray data using GRNmap and GRNsight.
* State the approach
** Give an overview of your team's contribution to GRNsight and why it is needed.

=== Combined Methods/Results/Discussion section ===

This section will summarize the entire workflow and findings for the project with contributions from all team members (Data Analyst, QA, and Coder/Designers). Create a combined flow chart of the tasks/milestones of each of the team members and then briefly describe the flow chart in the text.
* Number each of the figures sequentially and number each of the tables sequentially in order from first mention in the text. You can either embed your figures and tables in the appropriate place in the text or put them all at the end. Do not mix both styles, however.
* Write a descriptive legend for each figure and table that briefly states what the figure/table is and gives a brief key to any labels and abbreviations.

==== Data Analyst ====

* Table of ANOVA results from the [[Week 8]], discussing the interpretation of the p values.
* From the STEM analyis, include as figures the overall results (the screenshot showing all of the clusters) and then focus on the ones you interpreted for the [[Week 10]] journal assignment.
** Include a table showing the GO results for that cluster (just the narrowed down list of terms that you have interpreted).
** Discuss what the p values for the cluster and for the GO term list mean.
** Discuss the biological interpretation of your GO terms.
* Include a table that lists the transcription factors in your final network and their enrichment p value from YEASTRACT.
** Describe how and why you and your partner chose these transcription factors for your network.
** Include a figure (screenshot) of the unweighted networks visualized with GRNsight.
* GRNmap results
** Show the GRNsight visualization (screenshot) of the weighted networks, making sure that the genes are placed in the same relative location as each other an as the unweighted network figure.
** Provide the LSE:minLSE ratio
** Provide a table of the weights, P's, and b's
** Organize and show the individual expression plots
** Interpret the results of the model

==== Coders ====

* Give a narrative description of the new features created by the Coders.
** Provide code snippets and/or screenshots to illustrate as necessary.

==== QA ====

* Describe the data selected from each database for the gene page and the rationale for its inclusion.
* Describe any other tasks undertaken by the QA.

=== Conclusions ===

* Write a '''1-2''' page conclusion that summarizes the overall project and your findings.
** Overall, what have you learned about the regulation of cold shock in yeast?
** How does the new gene page feature facilitate the data analysis?
** What future directions would you take if you were to continue this project?
** Relate the results of your project to the papers you presented for journal club and included in your bibliography in [[Week 11]].
*** Did you discover anything new that wasn't reported in the journal club paper (Data Analyst/QA)?

=== Acknowledgments ===

Write a short paragraph acknowledging the assistance of anyone who is not a member of your team.

=== References ===

* This section lists all of the references cited in the text of the report (and only those references cited in the paper). Follow the [[Media: BIOL367_Fall2019_GuidelinesforLiteratureCitations.pdf | Guidelines for Literature Citations in a Scientific Paper]] handout for general principles.
* Remember that you need to cite anything for which you are not the original source. Generally, in the introduction, you should aim for a minimum of two in-text citations per paragraph. You may reference the course web site using the appropriate format for a web reference.
* List your references in alphabetical order by first author using the [https://owl.purdue.edu/owl/research_and_citation/apa_style/apa_formatting_and_style_guide/reference_list_basic_rules.html APA style] that you have been using throughout the semester. Note that the proper format must be followed for both articles and websites, just URLs are not sufficient.

== PowerPoint Presentation ==

Each team of students will prepare and give a 20 minute PowerPoint presentation to report the results of their project on Tuesday, December 10 at 2:00-4:00 PM.
* Please follow the [http://brightspace.lmu.edu Presentation Guidelines on Brightspace]] for how to format your slides.
* You will need to prepare ~20 slides (assume 1 slide per minute of presentation) and include the following content:
** Title slide that gives the main take-home message as the title of your presentation, the authors, date, and venue (course number and title).
** Outline slide that is a summary of take-home messages of your talk (should mirror your conclusion slide)
** The body of your talk (organized in a logical flow, not necessarily in the order given below).
*** Introduce the importance/significance of the problem and give background on cold shock in yeast. You can draw from the DA/QA's journal club presentation. This should follow the logic of the Introduction section of your group paper.
*** Show a combined flow chart of the tasks/milestones completed by your group (see Materials and Methods of group paper).
*** Show the table of ANOVA results.
*** Show the screenshot of the overall clustering results and the cluster you focused on .
*** Show a table of the GO results from that cluster, giving an interpretation.
*** Show the table of regulatory transcription factors in your network and their p values for enrichment.
*** Show the unweighted and weighted networks in GRNsight (with the genes arranged in the same way in each figure)
*** Database schema diagram for the MS Access database
** Conclusion slide that mirrors your outline
** Future directions
** Acknowledgments
** References
* '''''Your PowerPoint slides must be uploaded to the wiki and linked to from your individual journal page and your team page by 12:01am, Tuesday, December 10.'''''
** You can update your slides before your presentation, but we will be grading the ones you upload by the deadline.
* Your presentation (both the slides and the oral presentation) will be evaluated by the instructors using the [[Presentation Rubric]].
* Your presentation will also be evaluated by your fellow classmates (anonymously) who will answer the following questions:
*# What is the speaker's take-home message (one short sentence)?
*# What are the best points about the presentation's organization, visuals, and delivery? Please give at least 2 specific examples.
*# What points need improvement? Please give at least 2 specific examples.
* We expect that you will take the feedback from your previous presentation into account when doing this presentation.

== Individual Assessment and Reflection ==

Each person on the team will complete an assessment and reflection ''individually''. If you are comfortable with making this assessment publicly available, you may write it up as a wiki page or as a Word document uploaded to your group deliveables page. If you prefer to communicate your assessment privately, then email this to both Drs. Dahlquist and Dionisio.

=== Statement of Work ===

* Describe exactly what you did on the project.
* Provide references or links to artifacts of your work, such as:
** Wiki pages
** Other files or documents
** Code or scripts

=== Assessment of Project ===

* Give an objective assessment of the success of your project workflow and teamwork.
* What worked and what didn't work?
* What would you do differently if you could do it all over again?
* Evaluate your team’s portion of the GRNsight Gene Page Project and Group Report in the following areas:
*# Content: What is the quality of the work?
*# Organization: Comment on the organization of the project and of your group's wiki pages.
*# Completeness: Did your team achieve all of the project objectives? Why or why not?

=== Reflection on the Process ===

* What did you learn?
** With your head (biological or computer science principles)
** With your heart (personal qualities and teamwork qualities that make things work or not work)?
** With your hands (technical skills)?
* What lesson will you take away from this project that you will still use a year from now?

{{Final Project Links}}

[[Category:Group Projects]]

Final Project Deliverables

2019-11-19T19:23:16Z

Kdahlquist: /* Deliverables Checklist */ update with QA and Coder/Designer deliverables

{{ Under Construction }}
{{Final Project Links}}

== Deliverables Checklist ==

# Organized Team deliverables wiki page (or other media (CD or flash drive) with table of contents)
# Group Report (''.doc'', ''.docx'' or ''.pdf'' file)
# Individual statements of work, assessments, reflections (wiki page, ''.doc'', ''.docx'', ''.pdf'', or e-mailed to Dr. Dahlquist)
# Group PowerPoint presentation (given on Tuesday, December 10, ''.ppt'', ''.pptx'' or ''.pdf'' file)
# Excel spreadsheet with ANOVA results/stem formatting (''.xlsx'')
# PowerPoint of ANOVA table, screenshots of stem results (''.pptx''), screenshot of black and white GRNsight input network and colored GRNsight output networks
# Gene List and GO List files from each significant profile (''.txt'' compressed together in a ''.zip'' file)
# YEASTRACT "rank by TF" results (''.xlsx'')
# GRNmap input workbook (with network adjacency matrix, ''.xlsx'')
# GRNmap output workbook (''.xlsx'') and output plots (''.jpg'') zipped together
# MS Access database, unified by the three teams with expression tables and metadata table(s) created (''.accdb'').
# ReadMe for the database that describes the design of the database, references the sources of the data, and has a database schema diagram (''.doc'', ''.docx'', ''.pdf'')
# Query design for populating a GRNmap input workbook from the database (screenshot of MS Access, or SQL code, ''.txt'')
# Electronic notebook corresponding to these the microarray results files ([[Week 12/13]] and [[Week 15]]) to support ''reproducible research'' so that all manipulations of the data and files are documented so that someone else could begin with your starting file, follow the protocol, and obtain your results.

=== Grading ===

* The Group Report is worth 50 points.
* The Group PowerPoint presentation is worth 50 points.
* All other deliverables listed above, together, are worth 50 points.

== Group Report ==

* The report should be written with contributions from all group members.
* Submit as ''.doc'', ''.docx'' or ''.pdf'' file.

=== Style Sheet ===

Use the following guidelines when formatting your report:
* 2.54 cm (1 in) margins on all sides
* Double-spaced
* 12 point Times/Times New Roman font
* Number the pages on the lower-right corner
* Use left justification (“jagged” on the right side)

=== Title Page ===

Include the following information in a standalone title page:
* A descriptive title for your project
** The function of the title is to identify the main result or take-home message of the paper. It should be as specific as possible. It can be a phrase or a sentence. What is the main result of your paper that you want to convey with the title?
* The names of the team members (with middle initials)
* The course number and title of the class
* The date of submission

=== Introduction ===

The introduction gives the background information necessary to understand your report and should be '''2-3''' pages long (double-spaced). The introduction should be in the form of a logical argument that “funnels” from broad to narrow:

<gallery mode="nolines" widths=322px heights=256px>
Funnel.jpg
</gallery>

* States importance of the problem
* States what is known about the problem
** Give an overview of what is known about the regulation of the transcriptional response in yeast from your team's journal article and the annotated bibliography your team developed.
** Discuss how you will approach this by re-analyzing the yeast microarray data from your article.
* States what is unknown about the problem
** Which transcription factors belong in the gene regulatory network that controls the response in yeast is still unknown.
* States clues that suggest how to approach the unknown
** Introduce GRNmap and GRNsight as the answer to this problem.
* States the question the paper is trying to address
** In this case you want to discover new information about the microarray data using GRNmap and GRNsight.
* State the approach
** Give an overview of your team's contribution to GRNsight and why it is needed.

=== Combined Methods/Results/Discussion section ===

This section will summarize the entire workflow and findings for the project with contributions from all team members (Data Analyst, QA, and Coder/Designers). Create a combined flow chart of the tasks/milestones of each of the team members and then briefly describe the flow chart in the text.
* Number each of the figures sequentially and number each of the tables sequentially in order from first mention in the text. You can either embed your figures and tables in the appropriate place in the text or put them all at the end. Do not mix both styles, however.
* Write a descriptive legend for each figure and table that briefly states what the figure/table is and gives a brief key to any labels and abbreviations.

==== Data Analyst ====

* Table of ANOVA results from the [[Week 8]], discussing the interpretation of the p values.
* From the STEM analyis, include as figures the overall results (the screenshot showing all of the clusters) and then focus on the ones you interpreted for the [[Week 10]] journal assignment.
** Include a table showing the GO results for that cluster (just the narrowed down list of terms that you have interpreted).
** Discuss what the p values for the cluster and for the GO term list mean.
** Discuss the biological interpretation of your GO terms.
* Include a table that lists the transcription factors in your final network and their enrichment p value from YEASTRACT.
** Describe how and why you and your partner chose these transcription factors for your network.
** Include a figure (screenshot) of the unweighted networks visualized with GRNsight.
* Show the GRNsight visualization (screenshot) of the weighted networks, making sure that the genes are placed in the same relative location as each other an as the unweighted network figure.
* Interpret the results of the model as visualized in GRNsight.

==== Coders ====

* Give a narrative description of the new features created by the Coders.
** Provide code snippets and/or screenshots to illustrate as necessary.

==== QA ====

* Describe the data selected from each database for the gene page and the rationale for its inclusion.
* Describe any other tasks undertaken by the QA.

=== Conclusions ===

* Write a '''1-2''' page conclusion that summarizes the overall project and your findings.
** Overall, what have you learned about the regulation of cold shock in yeast?
** How does the new gene page feature facilitate the data analysis?
** What future directions would you take if you were to continue this project?
** Relate the results of your project to the papers you presented for journal club and included in your bibliography in [[Week 11]].
*** Did you discover anything new that wasn't reported in the journal club paper (Data Analyst/QA)?

=== Acknowledgments ===

Write a short paragraph acknowledging the assistance of anyone who is not a member of your team.

=== References ===

* This section lists all of the references cited in the text of the report (and only those references cited in the paper). Follow the [[Media: BIOL367_Fall2019_GuidelinesforLiteratureCitations.pdf | Guidelines for Literature Citations in a Scientific Paper]] handout for general principles.
* Remember that you need to cite anything for which you are not the original source. Generally, in the introduction, you should aim for a minimum of two in-text citations per paragraph. You may reference the course web site using the appropriate format for a web reference.
* List your references in alphabetical order by first author using the [https://owl.purdue.edu/owl/research_and_citation/apa_style/apa_formatting_and_style_guide/reference_list_basic_rules.html APA style] that you have been using throughout the semester. Note that the proper format must be followed for both articles and websites, just URLs are not sufficient.

== PowerPoint Presentation ==

Each team of students will prepare and give a 20 minute PowerPoint presentation to report the results of their project on Tuesday, December 10 at 2:00-4:00 PM.
* Please follow the [http://brightspace.lmu.edu Presentation Guidelines on Brightspace]] for how to format your slides.
* You will need to prepare ~20 slides (assume 1 slide per minute of presentation) and include the following content:
** Title slide that gives the main take-home message as the title of your presentation, the authors, date, and venue (course number and title).
** Outline slide that is a summary of take-home messages of your talk (should mirror your conclusion slide)
** The body of your talk (organized in a logical flow, not necessarily in the order given below).
*** Introduce the importance/significance of the problem and give background on cold shock in yeast. You can draw from the DA/QA's journal club presentation. This should follow the logic of the Introduction section of your group paper.
*** Show a combined flow chart of the tasks/milestones completed by your group (see Materials and Methods of group paper).
*** Show the table of ANOVA results.
*** Show the screenshot of the overall clustering results and the cluster you focused on .
*** Show a table of the GO results from that cluster, giving an interpretation.
*** Show the table of regulatory transcription factors in your network and their p values for enrichment.
*** Show the unweighted and weighted networks in GRNsight (with the genes arranged in the same way in each figure)
*** Database schema diagram for the MS Access database
** Conclusion slide that mirrors your outline
** Future directions
** Acknowledgments
** References
* '''''Your PowerPoint slides must be uploaded to the wiki and linked to from your individual journal page and your team page by 12:01am, Tuesday, December 10.'''''
** You can update your slides before your presentation, but we will be grading the ones you upload by the deadline.
* Your presentation (both the slides and the oral presentation) will be evaluated by the instructors using the [[Presentation Rubric]].
* Your presentation will also be evaluated by your fellow classmates (anonymously) who will answer the following questions:
*# What is the speaker's take-home message (one short sentence)?
*# What are the best points about the presentation's organization, visuals, and delivery? Please give at least 2 specific examples.
*# What points need improvement? Please give at least 2 specific examples.
* We expect that you will take the feedback from your previous presentation into account when doing this presentation.

== Individual Assessment and Reflection ==

Each person on the team will complete an assessment and reflection ''individually''. If you are comfortable with making this assessment publicly available, you may write it up as a wiki page or as a Word document uploaded to your group deliveables page. If you prefer to communicate your assessment privately, then email this to both Drs. Dahlquist and Dionisio.

=== Statement of Work ===

* Describe exactly what you did on the project.
* Provide references or links to artifacts of your work, such as:
** Wiki pages
** Other files or documents
** Code or scripts

=== Assessment of Project ===

* Give an objective assessment of the success of your project workflow and teamwork.
* What worked and what didn't work?
* What would you do differently if you could do it all over again?
* Evaluate your team’s portion of the GRNsight Gene Page Project and Group Report in the following areas:
*# Content: What is the quality of the work?
*# Organization: Comment on the organization of the project and of your group's wiki pages.
*# Completeness: Did your team achieve all of the project objectives? Why or why not?

=== Reflection on the Process ===

* What did you learn?
** With your head (biological or computer science principles)
** With your heart (personal qualities and teamwork qualities that make things work or not work)?
** With your hands (technical skills)?
* What lesson will you take away from this project that you will still use a year from now?

{{Final Project Links}}

[[Category:Group Projects]]