Icrespin Journal Week 9

Electronic Lab Notebook

Icrespin User Page

Assignment Page	Individual Journal Entry	Shared Journal Entry
Week 1	Icrespin Journal Week 1	Class Journal Week 1
Week 2	Icrespin Journal Week 2	Class Journal Week 2
Week 3	ILT1/YDR090C Week 3	Class Journal Week 3
Week 4	Icrespin Journal Week 4	Class Journal Week 4
Week 5	RNAct Week 5	Class Journal Week 5
Week 6	Icrespin Journal Week 6	Class Journal Week 6
Week 7	Icrespin Journal Week 7	Class Journal Week 7
Week 8	Icrespin Journal Week 8	Class Journal Week 8
Week 9	Icrespin Journal Week 9	Class Journal Week 9
Week 10	Icrespin Journal Week 10	Class Journal Week 10
Week 11	Icrespin Journal Week 11	FunGals
Week 12/13	Icrespin Journal Week 12/13	FunGals
Week 15	Icrespin Journal Week 15	FunGals

Purpose

The purpose of this week's assignment is to continue from week 8. Also, it is to better understand how STEM works, and how the user can analyze different profiles to comprehend cold shock.

Methods/Results

1. Viewing and Saving STEM Results
   1. 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 Alt and PrintScreen buttons to save the view in the active window to the clipboard) and paste it into a PowerPoint presentation to save your figures.
   2. 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 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 zip all the files together and upload them as one file).
2. Analyzing and Interpreting STEM Results
   1. 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?
        • Profile 1 (or in STEM it is profile 45) was selected because it was the first profile that comes up. It is interesting to see that it has an increase and then a sudden drop in its profile. It is cool to investigate because it isn't similar to the other profiles.
      • How many genes belong to this profile?
        • 406
      • How many genes were expected to belong to this profile?
        • 29.9
      • 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.
        • 0.00
      • 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?
        • 69/157
      • 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?
        • 9/157
      • Select the top 6 Gene Ontology terms from your filtered list (either p < 0.05 or corrected p < 0.05).
        • • Note whether the same GO terms are showing up in multiple clusters.
            • GO:0005730
            • GO:0042254
            • GO:0006364
            • GO:0030687
            • GO:0005634
            • GO:0000447
        • Look up the definitions for each of the terms at 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.
        • Copy and paste the GO ID (e.g. GO:0044848) into the search field on the left of the page.
        • In the 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. here.
          • GO:0005730
            • Name: Nucleolus
            • Definition: "A small, dense body one or more of which are present in the nucleus of eukaryotic cells. It is rich in RNA and protein, is not bounded by a limiting membrane, and is not seen during mitosis."
          • GO:0042254
            • Name: Ribosome biogenesis
            • Definition: "A cellular process that results in the biosynthesis of constituent macromolecules, assembly, and arrangement of constituent parts of ribosome subunits; includes transport to the sites of protein synthesis."
          • GO:0006364
            • Name:rRNA processing
            • Definition: "Any process involved in the conversion of a primary ribosomal RNA (rRNA) transcript into one or more mature rRNA molecules."
          • GO:0030687
            • Name: Preribosome, large subunit precursor
            • Definition: "A preribosomal complex consisting of 27SA, 27SB, and/or 7S pre-rRNA, 5S rRNA, ribosomal proteins including late-associating large subunit proteins, and associated proteins; a precursor of the eukaryotic cytoplasmic large ribosomal subunit."
          • GO:0005634
            • Name: Nucleus
            • Definition: "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated."
          • GO:0000447
            • Name: Endonucleolytic cleavage at A2
            • Definition: "Endonucleolytic cleavage between the SSU-rRNA and the 5.8S rRNA of an rRNA molecule originally produced as a tricistronic rRNA transcript that contained the Small SubUnit (SSU) rRNA, the 5.8S rRNA, and the Large SubUnit (LSU) rRNA, in that order, from 5' to 3' along the primary transcript."

Using YEASTRACT to Infer which Transcription Factors Regulate a Cluster of Genes (Tuesday, October 29)

In the previous analysis using STEM, we found a number of gene expression profiles (aka clusters) which grouped genes based on similarity of gene expression changes over time. The implication is that these genes share the same expression pattern because they are regulated by the same (or the same set) of transcription factors. We will explore this using the YEASTRACT database.

1. Open the gene list in Excel for the one of the significant profiles from your stem analysis. Choose a cluster with a clear cold shock/recovery up/down or down/up pattern. You should also choose one of the largest clusters.
   • Copy the list of gene IDs onto your clipboard.
2. Launch a web browser and go to the YEASTRACT database.
   • On the left panel of the window, click on the link to Rank by TF.
   • Paste your list of genes from your cluster into the box labeled ORFs/Genes.
   • Check the box for Check for all TFs.
   • Accept the defaults for the Regulations Filter (Documented, DNA binding plus expression evidence)
   • Do not apply a filter for "Filter Documented Regulations by environmental condition".
   • Rank genes by TF using: The % of genes in the list and in YEASTRACT regulated by each TF.
   • Click the Search button.
3. Answer the following questions:
   • In the results window that appears, the p values colored green are considered "significant", the ones colored yellow are considered "borderline significant" and the ones colored pink are considered "not significant". How many transcription factors are green or "significant"? 21
   • Copy the table of results from the web page and paste it into a new Excel workbook to preserve the results.
     • Upload the Excel file to the wiki and link to it in your electronic lab notebook.
     • Are CIN5, GLN3, and/or HAP4 on the list? If so, what is their "% in user set", "% in YEASTRACT", and "p value".CIN5: 12.16% for user set, 2.25% for YEASTRACT, p-value: 1 GLN3: 45.16% for user set, 7.55% for YEASTRACT, p-value: 4.11E-07, HAP4: 17.87% for user set, 6.60% for YEASTRACT, p-value: 0.062823
4. For the mathematical model that we will build, we need to define a gene regulatory network of transcription factors that regulate other transcription factors. We can use YEASTRACT to assist us with creating the network. We want to generate a network with approximately 15-20 transcription factors in it.
   • You need to select from this list of "significant" transcription factors, which ones you will use to run the model. You will use these transcription factors and add GLN3, HAP4, and ZAP1 if they are not in your list. Explain in your electronic notebook how you decided on which transcription factors to include. Record the list and your justification in your electronic lab notebook. Each group member will select a different network (they can have some overlapping transcription factors, but some should also be different).
   • Go back to the YEASTRACT database and follow the link to Generate Regulation Matrix.
   • Copy and paste the list of transcription factors you identified (plus HAP4, GLN3, and ZAP1) into both the "Transcription factors" field and the "Target ORF/Genes" field.
   • We are going to use the "Regulations Filter" options of "Documented", "Only DNA binding evidence"
     • Click the "Generate" button.
     • In the results window that appears, click on the link to the "Regulation matrix (Semicolon Separated Values (CSV) file)" that appears and save it to your Desktop. Rename this file with a meaningful name so that you can distinguish it from the other files you will generate.

Visualizing Your Gene Regulatory Networks with GRNsight

We will analyze the regulatory matrix files you generated above in Microsoft Excel and visualize them using GRNsight to determine which one will be appropriate to pursue further in the modeling.

1. First we need to properly format the output files from YEASTRACT.
   • Open the file in Excel. It will not open properly in Excel because a semicolon was used as the column delimiter instead of a comma. To fix this, Select the entire Column A. Then go to the "Data" tab and select "Text to columns". In the Wizard that appears, select "Delimited" and click "Next". In the next window, select "Semicolon", and click "Next". In the next window, leave the data format at "General", and click "Finish". This should now look like a table with the names of the transcription factors across the top and down the first column and all of the zeros and ones distributed throughout the rows and columns. This is called an "adjacency matrix." If there is a "1" in the cell, that means there is a connection between the trancription factor in that row with that column.
   • Save this file in Microsoft Excel workbook format (.xlsx).
   • For this adjacency matrix to be usable in GRNmap (the modeling software) and GRNsight (the visualization software), we need to transpose the matrix. Insert a new worksheet into your Excel file and name it "network". Go back to the previous sheet and select the entire matrix and copy it. Go to you new worksheet and click on the A1 cell in the upper left. Select "Paste special" from the "Home" tab. In the window that appears, check the box for "Transpose". This will paste your data with the columns transposed to rows and vice versa. This is necessary because we want the transcription factors that are the "regulatORS" across the top and the "regulatEES" along the side.
   • The labels for the genes in the columns and rows need to match. Thus, delete the "p" from each of the gene names in the columns. Adjust the case of the labels to make them all upper case.
   • In cell A1, copy and paste the text "rows genes affected/cols genes controlling".
   • Finally, for ease of working with the adjacency matrix in Excel, we want to alphabatize the gene labels both across the top and side.
     • Select the area of the entire adjacency matrix.
     • Click the Data tab and click the custom sort button.
     • Sort Column A alphabetically, being sure to exclude the header row.
     • Now sort row 1 from left to right, excluding cell A1. In the Custom Sort window, click on the options button and select sort left to right, excluding column 1.
   • Name the worksheet containing your organized adjacency matrix "network" and Save.
2. Now we will visualize what these gene regulatory networks look like with the GRNsight software.
   • Go to the GRNsight home page.
   • Select the menu item File > Open and select the regulation matrix .xlsx file that has the "network" worksheet in it that you formatted above. If the file has been formatted properly, GRNsight should automatically create a graph of your network. You can click the "Grid Layout" button to arrange the nodes in a grid, or you can click and drag the nodes (genes) around until you get a layout that you like and take a screenshot of the results. Paste it into your PowerPoint presentation.
     • If you have nodes (genes) floating around in the display that are not connected to any other nodes, we need to delete them from the network for the modeling to work properly. Go back to the Excel workbook and network sheet and delete both the row and column with the floating gene's name. Then re-upload the edited file to GRNsight to visualize it. Use this final version in your PowerPoint and subsequent modeling.

Data and Files

Excel file of dGLN3 data

Powerpoint of STEM profiles

dGLN3 Genelists

dGLN3 GOlists

Regulation Matrix

Text File of Profile 45

Network Excel Sheet

Conclusion

In the end, this week continued from week 8 assignment. User was able to learn about Gene Ontology and GRNSight. Data was clustered in gene lists and GO lists, then were sent to STEM. Afterwards, data was chosen based on different profiles. That specific profile was further analyzed and visualized using GRNSight. Overall, this assignment will be continued on in the next week to better understand the data.

Acknowledgments

This week my partners are John Nimmers-Minor and Michael Armas. We discussed about the GOlists and decided on each person's profile to research.

Thank you Dr. Dahlquist for helping with any analysis questions that were asked.

I copied and pasted methods from Week 9. Also, names and definitions were copied and pasted from Gene Ontology Resource.

"Except for what is noted above, this individual page was completed by me and not copied from another source."Icrespin (talk) 18:13, 28 October 2019 (PDT)

References

Gene Ontology. (1999). Gene Ontology Resource. Retrieved October 28, 2019, from http://geneontology.org/

GRNSight. (2019). GRNSight. Retrieved October 30, 2019, from https://dondi.github.io/GRNsight/

LMU BioDB 2019. (2019). Original Data. Retrieved October 25, 2019 from Media:BIOL367_F19_microarray-data_dGLN3.xlsx

LMU BioDB 2019. (2019). Week 9. Retrieved October 28, 2019, from https://xmlpipedb.cs.lmu.edu/biodb/fall2019/index.php/Week_9