LMU BioDB 2017 - User contributions [en]

Cazinge Final Reflection

2017-12-16T00:58:38Z

Cazinge: /* Individual Assessment and Reflection */ added response

== 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.
** I was responsible for the Journal Club report and the majority of the getGeneInformation part of the project, which was the function that the page used to retrieve data from the various databases. In addition, I provided assistance to the majority of the of the other groups as somewhat of a lead-developer on the project.

* Provide references or links to artifacts of your work:
**(Content referenced from John Lopez's individual reflection)
**[[Media:JLopezEAzingeJournalClub.pptx | The Week 11 presentation.]]
**[https://github.com/bhamilton18/GRNsight/blob/master/web-client/public/js/api.js The getGeneInformation() Code]
**[[Media:Gene_hAPI_Group_Report.pdf | The group paper.]]
**[[Media:Gene_hAPI_Final_Presentation.pptx | The group presentation]]
**[[Media:GetGeneInformationREADME.txt | README]]

=== Assessment of Project ===

* Give an objective assessment of the success of your project workflow and teamwork.
** I believe that our project was ultimately successful, given the amount of web development experience that the majority of the team necessarily had prior to the project. As discussed, I completed on the brunt of the workload required for the geneInformationFunction, but there was always some work that I could delegate to John; for example, he was responsible for the majority of the XML parsing present in the function, and ultimately learned about how our functions worked through doing so. All in all I think our team preformed well with Corinne keeping us on task and Dina doing the Biological work.
* What worked and what didn't work?
** In terms of what worked, I think that my taking a leading role in the project played a large role of our success within the constrained time period. Similarly, maintaining a hands-off relationship with John and allowing him to proceed at his own pace was more than beneficial to the end result of the project.
** In terms of what didn't work, I believe that we could have communicated more effectively than we did. By keeping ourselves up to date with not only the project's changing requirements, but also each other's progress, we may have been able to produce more cohesive and complete work within the given timeframe.
* What would you do differently if you could do it all over again?
** If I could start over, I'd establish stringent criteria in order to force the team to communicate with one another on a more rapid cycle.
* 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?
** The quality of the work is up to the par set by the projects deliverable expected from our team, as well as the milestones set out by the Coder's Wiki Page.
*# Organization: Comment on the organization of the project and of your group's wiki pages.
** Our organization, while not entirely perfect, was good enough in order to keep our group in sync and up to date at regular intervals.
*# Completeness: Did your team achieve all of the project objectives? Why or why not?
** Our group finished the assignment as completely as the scope of the project allowed. There was some data that the Project Managers would have liked to see from us, but as this data was not fed from the original data sources as mentioned from Week 9's homework assignment, it was not within the scope of this assignment to ensure the accessibility of that data.

=== Reflection on the Process ===

* What did you learn?
** With your head (biological or computer science principles)
*** The most important thing that I learned from this might just be how working with a group of people with differing skill sets operates on a conceptual level. It definitely wasn't easy staying in sync with the entire class, but we were successfully able to bridge the Bio/CompSci gap whenever necessary.
** With your heart (personal qualities and teamwork qualities that make things work or not work)?
*** I learned that it is important not only to be able to handle large quantities of work, but also be able to help other people while proceeding at their own pace. If I wasn't able to help the other groups with a calm and consistent demeanor, the entirety of our project might have ended up in a much worse place than it did.
** With your hands (technical skills)?
*** The sed command is now integral to my Bash developer tool kit, and I explicitly have this class to thank for that.
* What lesson will you take away from this project that you will still use a year from now?
*** Honestly I love the sed command, it's extremely liberating, and I can see myself using it even 5 years into the future.

Cazinge Final Reflection

2017-12-16T00:32:10Z

Cazinge: added template

Gene hAPI Deliverables

2017-12-16T00:30:48Z

Cazinge: /* Gene hAPI Deliverables */ added group report, individual statement, and pull request

[[Gene hAPI]]

Members:
*[[User:Cazinge|Eddie Azinge]]
*[[User:Dbashour|Dina Bashoura]]
*[[User:Johnllopez616|John Lopez]]
*[[User:Cwong34|Corinne Wong]]

= Gene hAPI Deliverables =
# Organized team deliverables wiki page
#* [[Gene_hAPI_Deliverables|Gene hAPI Deliverables page (this current page)]]
# Group report
#* [[Media:Gene_hAPI_Group_Report.pdf]]
# Individual statements
#* Eddie A.: [[Cazinge_Final_Reflection | Eddie's Individual Statement]]
#* Dina: [[Dbashour_Final_Reflection | Dina's Individual Statement]]
#* John: [[Johnllopez616_Individual_Statement|John's Individual Statement]]
#* Corinne: [[Cwong34_Individual_Statement|Corinne's individual statement]]
# Group PowerPoint presentation
#* [[Media:Gene_hAPI_Final_Presentation.pptx | Gene hAPI final presentation]]
#* [[Media:JLopezEAzingeJournalClub.pptx | Coders' Journal Club Presentation]] 
#* [[Media:Cold_Shock_Yeast_Genome_Response.pdf | QA/Data Analyst Journal Club Presentation]]
# Code (GitHub pull request)
#* [https://github.com/bhamilton18/GRNsight Github]
# README
#* [[Media:GetGeneInformationREADME.txt | getGeneInformation() Readme]]
# Excel spreadsheet with ANOVA results/stem formatting
#*[[Media:DGLN3_ANOVA_DB.xlsx | DGLN3 ANOVA/Stem]]
# PowerPoint of screenshots of stem results
#*[[Media:DGLN3_ppt_Dina.pptx | DGLN3 ppt Dina]]
# Gene List and GO List files from each significant profile
#*[[Media:Gene_List_GO_List_DB.zip | DGLN3 Gene List and GO list]]
# YEASTRACT "rank by TF" results
#*[[Media:Yeastract_results_TF_DB_Gene_hAPI.xlsx | Yeastract TF List]]
# GRNmap input workbook (with network adjency matrix)
#*[[Media:GRNmap_dGLN3_input.xlsx | GRNmap dGLN3 input]]
# GRNmap output workbook
#*[[Media:15-genes_32-edges_team-hAPI_Sigmoid_estimation_output.xlsx | GRNmap dGLN3 output]]
# Electronic notebooks
#* [[Dbashour_Week_8|Dina's Week 8]]
#* [[Dbashour_Week_10|Dina's Week 10]]
#* [[Dbashour_Week_11|Dina's Week 11]]
#* [[Dbashour_Week_12|Dina's Week 12]]
#* [[Dbashour_Week_14|Dina's Week 14]]
#* [[Dbashour_Week_15|Dina's Week 15]]

= 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 both Dr. Dahlquist and Dr. Dionisio)
# Group PowerPoint presentation (given on Tuesday, December 12, ''.ppt'', ''.pptx'' or ''.pdf'' file)
# Code (GitHub pull request)
#* Each team should coordinate in performing a final integration and integration testing iteration (see [[Coder]] milestone for details) which the Interaction and Integration team then submits to the ''original'' GRNsight GitHub repository as a single, unified pull request from the class project’s fork
# Supply a README that summarizes the functionality of your team's new feature (''.txt'' or ''.md'', ''one README per team'')
# Excel spreadsheet with ANOVA results/stem formatting (''.xlsx'')
# PowerPoint of screenshots of stem results (''.pptx'')
# 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'')
# Electronic notebook corresponding to these the microarray results files ([[Week 8]], [[Week 10]], and Weeks 11-15) 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.

=Group Report Update=
*1-2 page introduction
*Methods
**Data analyst
**QA
**Coders
*Combine results/discussion - add few sentences about significance after each result
**DA
**QA
**Coders
*Conclusion 1-2 pages
**Connect to JC papers
**Future direction
**Summary

[[Category: Gene hAPI]]

[[Category: Group Project]]

[[Category: Deliverables]]

File:Gene hAPI Group Report.pdf

2017-12-16T00:27:03Z

Cazinge:

Gene hAPI

2017-12-08T21:18:46Z

Cazinge: /* Executive Summaries */ adding weeks

==Gene hAPI==

===General Information===
Eddie (Cazinge):
*Role: Coder
*User Page: [[User:cazinge|Eddie Azinge]]

Dina (Dbashour):
*Role: Data Analysis
*User Page: [[User:dbashour|Dina Bashoura]]

Corinne (Cwong34):
*Role: Project Manager/Quality Assurance
*User Page: [[User:Cwong34|Corinne Wong]]

John (johnllopez616):
*Role: Coder
*User Page: [[User:johnllopez616|John Lopez]]

===Executive Summaries===
'''Eddie''':
* [[Cazinge Week 11|Week 11]]:
** This week, I finished the bulk of the function logic for our final project; I also performed research on the items necessary for an effective journal club presentation this coming Thursday by completing the individual assignment that we were assigned for the week.
** Getting a large portion of our designated assignment completed early worked pretty successfully to alleviate our stress over the impending assignment and place us ahead of the pace of other groups so that we would be able to work on the more secondary parts of our final presentation across the next 4 weeks.
** Working on the project without having a proper development environment set up ended up detracting from the fluidity of our collaboration as a team; as the versions of the final function that John and I worked on weren't linked via git, and were somewhat unruly to collaborate on.
** Next time, I'll make sure to set up my development environment according to Dondi's instructions on the Coders Guild Page.
[[User:Cazinge|Cazinge]] ([[User talk:Cazinge|talk]]) 23:39, 20 November 2017 (PST)
* [[Cazinge Week 12|Week 12]]:
** This week my task was to set up my development environment for the coder milestones. As such, I've completed milestones 1-3, talked to the other coders, and established a plan to work on the assignment in the future along with the rest of my team.
** Setting up milestones 1-3 went pretty well; since they were all standard open source project tasks, I was able to complete them simply, efficiently, and without any real friction.
** The only thing that I found less than desirable from this week was our communication; As Thanksgiving weekend rolls around we only stand to fall behind if we continue without amending our communication situation.
** This next week, I'll focus on keeping an open line of communication with the rest of my team, as well as completing the majority of the coding milestones that we have left.
[[User:Cazinge|Cazinge]] ([[User talk:Cazinge|talk]]) 23:39, 20 November 2017 (PST)
*[[Cazinge Week 14|Week 14]]:
**This week's we reached a good spot in terms of the execution of Milestone 4. I was the main point of contact with regards to general coding questions, and we're approaching the completion of the final assignment.
**This week was fun as I was able to leisurely interact with my classmates in an environment more typically representing that of actual software development. As such, I was able to help out with typical software development problems and speed up the development of the final project.
**I don't feel as if I struggled with any specific part of this week's assignment, but we haven't exactly been proactive about writing our tests, so that eventually needs to be resolved.
**As far as what needs to get done for this next week, testing. Other than that, I'm feeling good about our progress going into the last week.
[[User:Cazinge|Cazinge]] ([[User talk:Cazinge|talk]]) 13:18, 8 December 2017 (PST)
*[[Cazinge Week 15|Week 15]]:
**This week we finished the final functionality of the API function, marking our progress with the final project complete.
[[User:Cazinge|Cazinge]] ([[User talk:Cazinge|talk]]) 13:18, 8 December 2017 (PST)

'''Dina''':
* [[Dbashour_Week_11 | Week 11]]
** For this week, I assisted with creating our group wiki page, adding the template and setting up the page in preparation for the later weeks to come. I also located two out of 4 research articles regarding cold shock, yeast, and microarray data that we might later use for our final project. I became familiar with how to narrow down search queries in order to yield the smallest amount of results that are related to your actual topic, as well as identify how many articles are cited and how many articles cite the articles I found.
** I liked being able to work on the assignment in class, this way I was able to ask questions or clarify certain things that I needed help with. I also like that we have a guild of people, this way there is more of a support system if I am ever lost or need assistance.
** Because my task for this week was simply following the directions on the wiki, there was nothing that went wrong or needed fixing.
** Next week, I will work with my guild to present on our found articles and collaborate with them all in order to make our presentation run smoothly. 
[[User:Dbashour|Dbashour]] ([[User talk:Dbashour|talk]]) 23:43, 13 November 2017 (PST)
*[[Dbashour_Week_12 | Week 12]]
** For this week, Corrine and I prepared for the journal club presentation by individually reading the article "Comprehensive expression analysis of time-dependent responses in yeast cells to low temperature" by Sahara, T., Goda, T., & Ohgiya, S. After reading this article, I developed an outline that highlighted the main points in each section of the article. With Corrine, I made a presentation on that outline, making sure to include each highlighted point. I also located and defined 10 words in the article that I was unfamiliar with and presented a flowchart of the overall experimental design.
** What worked well for this week was to collaborate with Corrine on who was going to present which parts of the article, this way there was no confusion on that matter and the overall flow of the presentation would be well executed.
** There was a lack of communication with all of us as a team, so I am unsure as to what John and Eddie have done for this week.
** Next week, we will all communicate via text either with the entire class or with just my group in order to clarify what the role of each person is and if we have been completing our tasks. 
[[User:Dbashour|Dbashour]] ([[User talk:Dbashour|talk]]) 12:10, 21 November 2017 (PST)

'''Corinne''':
* [[Cwong34_Week_11|Week 11]]:
** This week I helped to create the template and group page. I found two sources to contribute to our annotated bibliography, which we can use to research information for our final project. I researched the accessibility and publishing details of the sources.
** It was nice that we had some time in class to work, so we could easily check in with each other about what we were working on. It was also nice that Dina and I were working on the same project, so we could easily understand our work and help each other.
** It was a bit difficult to be fully aware of the progress of everyone because we had separate projects for this week and next week too. Both halves of our group are working on separate presentations, so we have different focuses, which makes it harder to keep up with each other.
** Next week, since it will be a similar situation, I will try to keep up with the other half of my group by fully understanding their objectives/assignments for the week and checking in some more.
*[[Cwong34_Week_12|Week 12]]:
**This week, I read the "Comprehensive expression analysis of time-dependent responses in yeast cells to low temperature" by Sahara, T., Goda, T., & Ohgiya, S. I came up with a flow chart of their methods for the experiment and made an outline for my individual assignment. I also found ten terms I didn't know and wrote down their definitions in my individual journal. I worked with Dina on our presentation of the article over the weekend.
**Having a collaborative assignment worked for me and Dina because we knew exactly what we needed to do for the project this week, and we worked together to get it done. Moreover, we had more time to meet up with each other this weekend, so it was easier to collaborate.
**It was still difficult to keep in touch with all of the members of the group since we all had different things to work on.
**However, now that we are done with our separate presentations, it will be more of a focus to know what each person is working on. Moreover, I will work to create more communication between group members to keep track of our progress, whether it's on our gene page or over text.
[[User:Cwong34|Cwong34]] ([[User talk:Cwong34|talk]]) 23:26, 20 November 2017 (PST)

*[[Cwong34_Week_14|Week 14]]:
**This week, I met with the group members to check in on where we were in the project. I met with the other QAs, and we worked together to come up with a list of information to pull from databases, and specifically which information to pull from where.
**Having time to work on the project in class was very helpful to be able to meet with everyone. I could easily communicate to my team members and check in with them, but I could also check in with other teams to work together. There was a lot more communication this week, and I feel I have a good idea of our progression for the rest of the semester.
**Having to split the class time between working with our team members and working with our guilds was a bit of a challenge because there was a lot to cover between both groups. We made it work, but hopefully we'll be able to find a time to meet outside of class as well this coming week to work on our project.
[[User:Cwong34|Cwong34]] ([[User talk:Cwong34|talk]]) 00:25, 5 December 2017 (PST)

'''John''':
* [[Johnllopez Week 11|Week 11]]:
** This week my task was mostly focused around organizing the Journal Club Presentation. I created the initial outline for the powerpoint, created the style, and added over half the content to it. In addition, I spent some time with [[User:cazinge | Eddie Azinge]] who finished most of the deliverable portion of the assignment as he explained to me his process.
** I felt the assembling of the group was done so with ease because I worked with each of the people in the past on different assignments/projects. I felt like each of my members are dedicated and willing to work, so I'm glad that we have a good group. Eddie's experience in coding was an advantage for me because I shouldn't have any sort of confusion, however as I explain below, it is also a disadvantage.
** Despite the fact that Eddie provided an explanation for how he was able to develop the primary deliverable for the project, I felt a little disappointed that I couldn't go through the same discovery process he did. It essentially makes me feel like my role in the project isn't as important. In addition, I was also irritated that we didn't have many opportunities to work on the Journal Club Presentations in advance, for I felt like we weren't as prepared to create/give them as we could have been.
**Next week, I know that I have to get a head start on my individual portion of the assignment so that I'm not crunched for time like I was for the presentation. In addition, it's imperative that I review the code Eddie set up in detail so I can understand each line and how it works. Furthermore, I have to follow along with the other coders in setting up development rigs.
[[User:Johnllopez616|Johnllopez616]] ([[User talk:Johnllopez616|talk]]) 23:41, 13 November 2017 (PST)

* [[Johnllopez Week 12|Week 12]]:
** This week my task involved setting up my development environment for the later milestones. It included the establishment of milestones 1-3, talking to the other coders, and establishing a plan to work on the assignment in the future.
** I felt like the setting up of the environment went well. Once I understood what I had to do, it was not difficult to set up the environment. In addition, I had no problems setting it up on GitHub and my computer once it happened.
** Unfortunately I felt like this week wasn't a very productive week. Despite setting up the environment, I did not collaborate well with my teammates. We have yet to arrange a proper work schedule and plan, especially to ensure that over the thanksgiving break we are able to do some stuff. This has led to the group's progress existing in a state of limbo.
** On Thursday, I asked the class to obtain a WhatsApp to allow for communication. This will be essential to communicate between the coders to accomplish the project. On Tuesday I will connect with as many people in the class. More importantly, I will get my group together to work on a set plan to work on the assignment and establish deadlines. Although this week wasn't the most productive for me, I will ensure that we don't fall behind next week.
[[User:Johnllopez616|Johnllopez616]] ([[User talk:Johnllopez616|talk]]) 23:27, 20 November 2017 (PST)

*[[johnllopez Week 14|Week 14]]:
**This week's primary objective was to get as much of Milestone 4 as finished as possible. My portion of the assignment was to learn how to extract data from XML files and translate it so that the page developers could use it.
**Perhaps the best part about this portion of the assignment was being able to discover a bit of jQuery and DOM functions. Knowledge and exposure to both of these is fundamental in obtaining a potential job.
**One area where I struggled was figuring out how to extract JSON using javascript. This is something that both of the Eddies, however, were proficient in doing, so it's important that I seek their help in understanding this concept. In addition, I wish I communicated more with the coders during the development of the project.
**This week will be crucial in finishing Milestone 4 for the project. It's absolutely necessary that the coders and I not only integrate everything, but that I am following along with what they do. I will ask a lot of questions to truly understand what's happening.
[[User:Johnllopez616|Johnllopez616]] ([[User talk:Johnllopez616|talk]]) 23:16, 4 December 2017 (PST)

==Journal Club Deliverable==
[[Media:JLopezEAzingeJournalClub.pptx | The presentation - Coder/Designer]] 
[[Media:Cold_Shock_Yeast_Genome_Response.pdf | Journal Club Week 12 Presentation - QA/Data Analyst]]

== Journal Club Article ==
Sahara, T., Goda, T., & Ohgiya, S. (2002). Comprehensive expression analysis of time-dependent genetic responses in yeast cells to low temperature. Journal of Biological Chemistry, 277(51), 50015-50021.
 
{{template:Gene_hAPI}}

Template:Gene hAPI

2017-12-08T21:03:49Z

Cazinge: /* Coders */ updated list

= Gene hAPI Helpful Links =
Our Deliverables: [[Gene hAPI Deliverables]]

Main Page: [[Main Page]]

Project Page: [[GRNsight Gene Page Project]]

[http://dondi.github.io/GRNsight/ GRNsight]

=Project 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 both Dr. Dahlquist and Dr. Dionisio) 
[ ]Group PowerPoint presentation (given on Tuesday, December 12, .ppt, .pptx or .pdf file) 
[ ]Code (GitHub pull request) 
[ ]Each team should coordinate in performing a final integration and integration testing iteration (see Coder milestone for details) which the Interaction and Integration team then submits to the original 
[ ]GRNsight GitHub repository as a single, unified pull request from the class project’s fork 
[ ]Supply a README that summarizes the functionality of your team's new feature (.txt or .md, one README per team) 
[ ]Excel spreadsheet with ANOVA results/stem formatting (.xlsx) 
[ ]PowerPoint of screenshots of stem results (.pptx) 
[ ]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) 
[ ]Electronic notebook corresponding to these the microarray results files (Week 8, Week 10, and Weeks 11-15) 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. 

=Project Manager=
[ ]'''Milestone 1: Project “Scaffolding”'''

This milestone pertains to setting up an initial schedule and any resources that your team will use for the duration of the project. It will be useful to get an overview of every team member’s own milestones so that you have an accurate big picture view.

# In consultation with your team, work backward from the final deadline to set intermediate deadlines for each deliverable. In particular you need to set deadlines for what you will accomplish by the journal deadline for [[Week 12]], [[Week 14]], and [[Week 15]].
# Organize management tools for your team:
#* Workflow narratives
#* Action items
#* Testing results/reports
#** Bugs/feature requests
#** Question/answer sequences

[ ]'''Milestone 2: Periodic Updates'''

Not as much a milestone as an on-going task, once the project is up and running the Project Manager is responsible for keeping track of everyone’s progress.

# Get periodic updates on progress; in particular, the project’s “place” in the overall flow should be known at all times (transparency). Team members will be giving a status reports in class for the rest of the semester. However, the instructors will expect you to know and be able to report on the status of each member of your team at any time.
# Familiarize yourselves with the specific milestones of [[Quality Assurance|each]] [[Coder|team]] [[Data Analysis|member]] so that you know how to monitor the team’s overall progress.
# Monitor the status of the report-in-progress and other related documentation.
# Coordinate team decisions and action items addressing any unforeseen delays or roadblocks.

=Data Analyst=
[X]'''Milestone 1: Annotated Bibliography'''

* For the [[Week 11]] assignment, the Data Analysts will work with the QAs to develop an annotated bibliography of papers that perform the global transcriptional analysis of DNA microarray data in yeast.

[X]'''Milestone 2: Journal Club Presentation'''

* For the [[Week 12]] assignment, the Data Analysts will work with the QAs to prepare a PowerPoint presentation to be delivered in class on Tuesday, November 21.

[ ]'''Milestone 3: Complete Data Analysis of Dahlquist Lab Data for Visualization with GRNsight'''

* For [[Week 14]] and [[Week 15]], the Data Analysts will review and complete the microarray data analysis begun with the [[Week 8]] and [[Week 10]] assignments with the eventual goal of visualizing a gene regulatory network derived from their dataset in GRNsight. Specifically:
*# Review the ANOVA results from [[Week 8]] for accuracy, making corrections if necessary.
*# If corrections were made to the ANOVA results, re-running stem ([[Week 10]]).
*# Using the [http://www.yeastract.com YEASTRACT Database] to determine which transcription factors are candidates for regulating the genes in a cluster from stem (part of [[Week_10#Using_YEASTRACT_to_Infer_which_Transcription_Factors_Regulate_a_Cluster_of_Genes | Week 10]] that we postponed.
*# Using the [http://www.yeastract.com YEASTRACT Database] to develop a candidate gene regulatory network (part of [[Week_10#Visualizing_Your_Gene_Regulatory_Networks_with_GRNsight | Week 10]] that we postponed).
*# Using the [http://kdahlquist.github.io/GRNmap/ GRNmap] software to model the gene regulatory network (confer with Dr. Dahlquist when you are ready for this).
*# Visualizing the results with [http://dondi.github.io/GRNsight/ GRNsight].
* As the end-user of the GRNsight software, the Data Analysts will provide feedback to the QAs and Coders about the usability of the new features.

=Quality Assurance=
[X]'''Milestone 1: Annotated Bibliography'''

* For the [[Week 11]] assignment, the Data Analysts will work with the QAs to develop an annotated bibliography of papers that perform the global transcriptional analysis of DNA microarray data in yeast.

[X]'''Milestone 2: Journal Club Presentation'''

* For the [[Week 12]] assignment, the Data Analysts will work with the QAs to prepare a PowerPoint presentation to be delivered in class on Tuesday, November 21.

[ ]'''Milestone 3: Requirements Analysis'''

As the Coders begin their development work and the Data Analysts start working with their assigned microarray data sets, the QA team members should familiarize themselves with and help specify the expected correct functionalities of their respective teams.

* The ''entire QA guild'' should become an expert on the information that can be retrieved for a gene, so that they know how IDs should look, how certain data types will be displayed, etc. This will help them detect flaws and areas of improvement as development work proceeds.
* ''Gene Database APIs team'': QA should get to know how the four web service APIs are to be used in order to retrieve gene data given a gene symbol. They should be able to perform these steps themselves using '''curl''' or a web browser, so that they can provide independent verification that the Coders’ work is functioning correctly.

[ ]'''Milestone 4: On-going Testing of Respective Team Deliverables'''

The rest of the semester is expected to be an on-going process of verifying and validating the correctness of a QA member’s assigned team. Specific concerns include but are not limited to the following:
* ''Gene Database APIs and JASPAR teams'': Manual testing will involve some combination of '''curl''' and web browser developer tool use in order to get to know the data returned by the various web services. The Coder members should find ways to show the Quality Assurance members the work-in-progress data returned by <code>getGeneInformation</code>, which QA can then compare to the raw web service API calls for accuracy. After the first integration milestone, QA members can examine the behavior of the prototype gene page at both the end-user and Developer Tools levels (i.e., examining network traffic to make sure the correct requests are going out with the expected responses coming in; inspecting the gene page elements to make sure that they received the correct data).

=Coders=
[X]'''Milestone 0: Journal Club Presentation'''

* For the [[Week 11]] assignment, the Coders (including Designers) of each team will prepare a PowerPoint presentation of their respective assigned software design/development/engineering/best practices reading to be delivered in class on Tuesday, November 14.

[X]'''Milestone 1: Working Environment Setup'''

Coder work will require the following software. The Seaver 120 lab computers are already set up for this; this list is provided for Coders who need to work on a different computer or outside of the lab.
* Node.js 8.4.0 or newer
* Code-savvy editor such as Atom or Microsoft Visual Studio Code
* Web browser with developer tools (Seaver 120 uses Google Chrome)
* '''git''' version control software
* (depends on team) '''curl''' command

Make sure that this software is installed and operational before beginning. If any Coder needs help with any of these packages, please consult your fellow guild members or ask Dr. Dionisio.

[X]'''Milestone 2: Version Control Setup'''

Coding work will be done on a ''fork'' of the open source [https://github.com/dondi/GRNsight GRNsight project], which is hosted on [https://github.com GitHub]. The software that interacts with GitHub to perform version control is '''git'''. If any Coder needs help with '''git''' or version control concepts in general, please consult your fellow guild members or ask Dr. Dionisio.

The Interaction and Integration team is responsible for this fork, and will do their own work on the ''master'' branch of this fork. Thus, many of the early steps in this procedure involve them, and they must accomplish these steps first before the others can proceed:

# All members of the Coder guild should acquire a GitHub account, if they don’t already have one (it’s free).
# One of the Coders of the Interaction and Integration team creates a fork of the GRNsight project.
# The Interaction and Integration team adds the GitHub accounts of all Coder guild members as ''collaborators'' on the fork.
# Once every team is a collaborator on this fork, they can then create their respective ''branches'' on the fork:
#* The Page Design team creates a branch called ''page-design''.
#* The Gene Database APIs team creates a branch called ''gene-database-apis''.
#* ''After'' the ''gene-database-apis'' branch is created, the JASPAR API team creates a branch '''from ''gene-database-apis''''' called ''jaspar-api''.

The teams will then do their work on their respective branches. The protocol for integrating your work is described in the ''Integration and Integration Testing'' milestone below.

The structure described in this milestone is a typical GitHub fork-and-branch approach which some Coders are already familiar with. If any Coder needs help with this methodology, please consult your fellow guild members or ask Dr. Dionisio.

[X]'''Milestone 3: “Developer Rig” Setup and Initial As-Is Build'''

Before development can begin in earnest, the following initial setup steps must be performed ''per Coder'' after version control setup is complete:
# '''git clone''' the GitHub fork created in the version control setup milestone on where you plan to work on the project.
# '''cd''' into the folder that contains the cloned repository
# '''git checkout''' the branch that is assigned to you:
#* Interaction and Integration: ''master'' (no need for an explicit checkout because the clone defaults to this branch)
#* Page Design: ''page-design''
#* Gene Database APIs: ''gene-database-apis''
#* JASPAR: ''jasper-api'' (note that as instructed above, '''this should be a branch of the ''gene-database-apis'' branch''')
# Follow the [https://github.com/dondi/GRNsight/wiki/Running-the-Applications instructions in the GRNsight wiki] to perform the following:
## Installation of necessary third-party libraries
## Initial startup of the GRNsight application ''on your computer''

If anyone runs into problems with this procedure, please consult with your fellow guild members or notify Dr. Dionisio.

[ ]'''Milestone 4: Development, Implementation, and Localized Testing'''

At this point, each team proceeds with their own development work. In general, each team will do most of their work (and place most of their files) in the following locations within the GRNsight repository. Some files may need to go elsewhere; check in with Dr. Dionisio if there are any file organization/location issues:

* Page Design: ''web-client/public/html'' (this folder does not yet exist; you will need to create it) and ''web-client/test'' (in case you are able to write unit tests for your work)
** While running GRNsight, you will be able to access files in this folder through the URL http://localhost:5001/html/''filename''
* Gene Database APIs: ''web-client/public/js'' and ''web-client/test''
* JASPAR: ''web-client/public/js'' and ''web-client/test''
* Interaction and Integration: Because you are integrating the other teams’ work, you will likely interact with the entire ''web-client'' folder

For the Gene Database APIs and JASPAR teams, your work will not be visible to end-users until the first overall integration is done, so use ''unit tests'' as your initial approach for verifying that your code is working well. You will probably need to mock up your API calls so that you don’t bombard the web services with requests while unit testing. However, these sites are generally open access so you can probably write your ''initial'' set of tests using real requests and responses.

The Quality Assurance members of each team should take charge of manual testing. Specific suggested manual testing tasks are described in the [[Quality Assurance]] guild page.

The Interaction and Integration team should initially get to know the files being created and worked on by the other teams prior to the first integration milestone.

Teams should perform their own internal testing (both manual and automated) on their respective files throughout development. Commit and push your work to your designated branch at appropriate intervals. Coders who are not familiar with this style of working should consult with fellow guild members or talk to Dr. Dionisio.

[ ]'''Milestone 5: Integration and Integration Testing'''

Integration and integration testing should take place periodically to make sure that the overall gene page project is coming together well. Most of the Coders’ work for the remainder of the semester will be a cycle of the development and integration milestones. The Interaction and Integration team is in charge of coordinating when these integrations take place. The first integration will likely be the most difficult one to perform, but it is hoped that later iterations will proceed more smoothly.

When the other teams have reached the following sub-milestones, the first integration cycle can take place. Future iterations are at the discretion of the Interaction and Integration team in coordination with how the other teams are progressing:
* Page Design: first working version of the gene page
* Gene Database APIs: first working version of the <code>getGeneInformation</code> function
* JASPAR: This team’s work does not have to participate in the first integration cycle; instead, they should integrate their work with the Gene Database APIs team whenever they have a working prototype. JASPAR functionality simply “shows up” at some future integration cycle.
* Interaction and Integration: You are not blocked from working prior to the first integration; you can work on exploring the existing GRNsight code to implement the right-click functionality that will open the gene page in a new tab or window. You can start by making this code open a blank page. Upon the first integration iteration, you can then connect that page to the gene page provided by the Page Design team.

An individual integration iteration proceeds in this way:
# The Page Design and Gene Database APIs teams issue ''pull requests'' to merge their branches with the ''master'' branch.
# The Coders guild reviews each other’s code and points out any needed revisions.
# Once all revision requests are fulfilled, the Interaction and Integration team merges the pull requests into ''master''.
# The Coders guild resolves any merge conflicts then tests the combined code.
# The combined code is allowed to be ''unfinished'', but it should not be ''broken''. The combined GRNsight build should still run and show steady progress toward the fully-envisioned gene page functionality.
# Code revisions at this stage are committed and pushed to ''master''.
# When the Interaction and Integration team is satisfied with the state of the combined code, the other teams can then merge ''master'' back into their respective branches, and localized development and testing can proceed to the next phase.
# When the teams feel that they have reached another integration point, they notify the Interaction and Integration team of this and another integration iteration can take place.
# Rinse and repeat until the project is done!

[[Category:Gene hAPI]]

[[Category:Group Projects]]

Cazinge Week 15

2017-12-08T21:01:02Z

Cazinge: adding response

==Electronic Notebook==

For this week, I finished up the code for the API, along with filtering the data requested by the data analysis team. Aside from assisting other teams with their work, there was nothing of note accomplished other than this for the week.

==Acknowledgments==

I worked with no other team member on this assignment. This individual user entry was completed by me and not copied from another source.
[[User:Cazinge|Cazinge]] ([[User talk:Cazinge|talk]]) 13:01, 8 December 2017 (PST)

==References==

LMU BioDB 2017. (2017). Week 15. Retrieved November 30, 2017, from https://xmlpipedb.cs.lmu.edu/biodb/fall2017/index.php/Week_15

{{Template:Cazinge}}

Cazinge Week 14

2017-12-08T20:59:07Z

Cazinge: Adding Electronic Notebook

==Electronic Notebook==

For this week, the majority of my time was spent setting up the pre-requisite API calls for our final project. This involved translating the already existing code to instead use jQuery; as well as assisting other groups in completing their portion of the code. In total, I created the main function, getGeneInformation, along with completing 3/5 of the helper functions that I stubbed out for retrieving data from each of the targeted databases. Going forward, all we need to do is format the data and we should be done with the project.

==Acknowledgments==

I worked with no other team member on this assignment. This individual user entry was completed by me and not copied from another source.
[[User:Cazinge|Cazinge]] ([[User talk:Cazinge|talk]]) 12:59, 8 December 2017 (PST)

==References==

LMU BioDB 2017. (2017). Week 14. Retrieved November 23, 2017, from https://xmlpipedb.cs.lmu.edu/biodb/fall2017/index.php/Week_14

{{Template:Cazinge}}

Cazinge Week 12

2017-12-08T20:57:22Z

Cazinge: adding template

==Electronic Lab Notebook==

[[Gene hAPI|Week 12(Abridged Summary)]]:

In no particular order, I
* Verified that Node 8.4.0+ was running on my machine
* Verified that I had at least one text editor on my machine (Atom)
* Verified that I had Google Chrome on my machine
* Verified that I had git installed on my machine
* Verified that I had curl installed on my machine
* Participated in adding my github account to the forked repository as a contributor
* Created a fork off of the master branch for our gene database function
* Cloned the repository onto my machine
* Installed the third-party libraries
* Verified that GRNsight would be able to run on my computer.

This was all standard procedure and will allow me to contribute effectively in the future.

==Acknowledgments==

I worked with no other team member on this assignment. This individual user entry was completed by me and not copied from another source.
[[User:Cazinge|Cazinge]] ([[User talk:Cazinge|talk]]) 23:50, 20 November 2017 (PST)

==References==

LMU BioDB 2017. (2017). Week 12. Retrieved November 17, 2017, from https://xmlpipedb.cs.lmu.edu/biodb/fall2017/index.php/Week_12

{{Template: Cazinge}}

Template:Cazinge

2017-12-08T20:50:14Z

Cazinge: updating to weeks 14/15

Template:Cazinge

2017-12-08T20:49:07Z

Cazinge: /* My Links */ added weeks 13 and 14

Cazinge Week 12

2017-11-21T07:51:56Z

Cazinge: /* Electronic Lab Notebook */ updating link

Cazinge Week 12

2017-11-21T07:50:09Z

Cazinge: Created page with content

==Electronic Lab Notebook==

[[White Chicks|Week 12(Abridged Summary)]]:

In no particular order, I
* Verified that Node 8.4.0+ was running on my machine
* Verified that I had at least one text editor on my machine (Atom)
* Verified that I had Google Chrome on my machine
* Verified that I had git installed on my machine
* Verified that I had curl installed on my machine
* Participated in adding my github account to the forked repository as a contributor
* Created a fork off of the master branch for our gene database function
* Cloned the repository onto my machine
* Installed the third-party libraries
* Verified that GRNsight would be able to run on my computer.

This was all standard procedure and will allow me to contribute effectively in the future.

==Acknowledgments==

I worked with no other team member on this assignment. This individual user entry was completed by me and not copied from another source.
[[User:Cazinge|Cazinge]] ([[User talk:Cazinge|talk]]) 23:50, 20 November 2017 (PST)

==References==

LMU BioDB 2017. (2017). Week 12. Retrieved November 17, 2017, from https://xmlpipedb.cs.lmu.edu/biodb/fall2017/index.php/Week_12

Gene hAPI

2017-11-21T07:39:18Z

Cazinge: /* Executive Summaries */ added responses

Template:Cazinge

2017-11-16T23:09:19Z

Cazinge: /* My Links */ Updating Links

White Chicks

2017-11-16T23:06:05Z

Cazinge: /* Executive Summaries */ added signature

==gene hAPI==

===General Information===
Eddie (Cazinge):
*Role: Coder
*User Page: [[User:cazinge|Eddie Azinge]]

Dina (Dbashour):
*Role: Data Analysis
*User Page: [[User:dbashour|Dina Bashoura]]

Corinne (Cwong34):
*Role: Project Manager/Quality Assurance
*User Page: [[User:Cwong34|Corinne Wong]]

John (johnllopez616):
*Role: Coder
*User Page: [[User:johnllopez616|John Lopez]]

===Executive Summaries===
'''Eddie''':
* [[Cazinge Week 11|Week 11]]:
** This week, I finished the bulk of the function logic for our final project; I also performed research on the items necessary for an effective journal club presentation this coming Thursday by completing the individual assignment that we were assigned for the week.
** Getting a large portion of our designated assignment completed early worked pretty successfully to alleviate our stress over the impending assignment and place us ahead of the pace of other groups so that we would be able to work on the more secondary parts of our final presentation across the next 4 weeks.
** Working on the project without having a proper development environment set up ended up detracting from the fluidity of our collaboration as a team; as the versions of the final function that John and I worked on weren't linked via git, and were somewhat unruly to collaborate on.
** Next time, I'll make sure to set up my development environment according to Dondi's instructions on the Coders Guild Page.
[[User:Cazinge|Cazinge]] ([[User talk:Cazinge|talk]]) 15:06, 16 November 2017 (PST)

'''Dina''':
* [[Dbashour_Week_11 | Week 11]]
** For this week, I assisted with creating our group wiki page, adding the template and setting up the page in preparation for the later weeks to come. I also located two out of 4 research articles regarding cold shock, yeast, and microarray data that we might later use for our final project. I became familiar with how to narrow down search queries in order to yield the smallest amount of results that are related to your actual topic, as well as identify how many articles are cited and how many articles cite the articles I found.
** I liked being able to work on the assignment in class, this way I was able to ask questions or clarify certain things that I needed help with. I also like that we have a guild of people, this way there is more of a support system if I am ever lost or need assistance.
** Because my task for this week was simply following the directions on the wiki, there was nothing that went wrong or needed fixing.
** Next week, I will work with my guild to present on our found articles and collaborate with them all in order to make our presentation run smoothly. 
[[User:Dbashour|Dbashour]] ([[User talk:Dbashour|talk]]) 23:36, 13 November 2017 (PST)

'''Corinne''':
* [[Cwong34_Week_11|Week 11]]:
** This week I helped to create the template and group page. I found two sources to contribute to our annotated bibliography, which we can use to research information for our final project. I researched the accessibility and publishing details of the sources.
** It was nice that we had some time in class to work, so we could easily check in with each other about what we were working on. It was also nice that Dina and I were working on the same project, so we could easily understand our work and help each other.
** It was a bit difficult to be fully aware of the progress of everyone because we had separate projects for this week and next week too. Both halves of our group are working on separate presentations, so we have different focuses, which makes it harder to keep up with each other.
** Next week, since it will be a similar situation, I will try to keep up with the other half of my group by fully understanding their objectives/assignments for the week and checking in some more.

'''John''':
* Week 11:
**

{{template:White_Chicks}}

Cazinge Week 11

2017-11-14T08:08:01Z

Cazinge: /* Article Outline */ adding content

=Presentation Prep=

==Unknown Terms==
* RFC 5321
** A specification of the basic protocol for Internet electronic mail transport. (1)
* plug-in
** A software component that adds a specific feature to an existing computer program. (2)
* documentation
** The information that describes the product to its users, consisting of product technical manuals and online information. (3)
* startups
** A young company that is just beginning to develop, usually small and initially financed and operated by a handful of founders or one individual.(4)
* bytecode
** Computer object code that is processed by a program, which converts each generalized machine instruction into a specific machine instruction that the computer's processor will understand. (5)
* Ajax
** A client-side script that communicates to and from a server/database without the need for a complete page refresh.(6)
* Web server
** A computer that is connected to the Internet, can be accessed through a domain name, and stores a website's component files and delivers them to the end-user's device. (7)
* Interpreter
** A program that executes instructions written in a high-level language. (8)
* Lisp
** A programming language that was designed for easy manipulation of data strings.(9)
* PostgreSQL
** An open source object-relational database system.(10)

==Article Outline==
* Software Development encompasses more than just the end-product itself
** Software development often encompasses more than just building a spec as previously defined.
** In order to properly carry out their tasks, Software developers need to understand the projects that they're building.
*** For example, Data can be stored, retrieved, accessed, and queried in a language similar to one that humans are generally comfortable with via SQL.
*** Creating schemas that encompass the use-cases of these specifications in a simple/effective manner allows one to create large applications such as Amazon. (5.2)
*** Understanding these projects requires a large repertoire of skills and languages known.
** Software development projects require communication in order to be conducted efficiently.
*** By having a baseline knowledge in the field of software engineering, one can have meaningful conversations like the one held between programmers A, B, and C. (5.1)
** In order to work effectively and build efficient projects, developers need to account for and understand all potential edge cases that could cause their programs to malfunction.
** Portable and extensible languages are often the most popular
*** Java gained the majority of its popularity due to its modularity, extensibility, platform support, and universal presence.
**** Multiple languages and projects were able to be developed with Java as a backbone
***** Jython
***** Jruby
***** Scala
***** Clojure
**** Research students, open source contributors, and hobbyists all contributed to the Java ecosystem indirectly, which benefit the community at large indirectly.
*** Javascript became the language that Java had originally aimed to be with it's applets
**** Extensible NPM package system lent itself towards open source projects, shared code, and universal adoption
**** Optimizations from Google resulted in the V8 engine, making Javascript a practical language for high speed computations.
**** Node.js enabled Javascript as a universal language, covering both general application logic, front-end, and back-end altogether. (5.7)
** Ford's article educates proper software development best practices through context and historical rather than through diatribe against bad practices
** He outlines the current status of open science and reproducible research, and we should follow suit making sure that the fruits of our labor are accessible to the Software development community en masse.

==Executive Summary==
[[White Chicks|Week 11 (Abridged Summary)]]:
* As soon as I saw the assignment that my assigned group was given for our contribution to the GRNsight Gene Page assignment I began work on it, as I realized that the scope of the project could be accomplished somewhat succinctly with a proper approach to developing the specific functionality. The first thing that I did was divide the function into 4 helper functions, one dictionary mapping to facilitate retrieving the data, and a final function that was left unimplemented for future filtering and transformations of the data sources. After that, John and I went through the class' documented methods for reaching the final outcome of generating the gene page for a given gene symbol for each of the 4 databases. I was in charge of realizing 3 of them, as well as the scaffolding for the rest of the function, while John handled the last API in order to gain familiarity with the paradigm and working on a project of this scale. Due to my prior understanding of the technology we would use to create this function, I was successfully able to complete each of the 3 sub-functions that I was assigned, enabling users to gain data about any gene across the 3 of the 4 databases that we were assigned to work on. We have yet to interact with the JASPAR API team, but believe that the content left for the core assignment shouldn't be too high maintenance to finish from here. Outside of that, John and I worked collaboratively on the Journal Club presentations via our individual journals and by meeting together outside of class.

==Presentation Link==

https://xmlpipedb.cs.lmu.edu/biodb/fall2017/index.php/File:JLopezEAzingeJournalClub.pptx

==Acknowledgements==

I met with my homework partner John Lopez outside of class and worked on the presentation together. While I worked with the person noted above, this individual user entry was completed by me and not copied from another source.

[[User:Cazinge|Cazinge]] ([[User talk:Cazinge|talk]]) 23:59, 13 November 2017 (PST)

==References==

LMU BioDB 2017. (2017). Week 11. Retrieved November 10, 2017, from https://xmlpipedb.cs.lmu.edu/biodb/fall2017/index.php/Week_11

===Unknown Terms References===
# Simple Mail Transfer Protocol. (n.d.). Retrieved November 14, 2017, from https://tools.ietf.org/html/rfc5321.html
# Plug-in (computing). (2017, November 08). Retrieved November 14, 2017, from https://en.wikipedia.org/wiki/Plug-in_(computing)
# What is documentation? - Definition from WhatIs.com. (n.d.). Retrieved November 14, 2017, from http://searchsoftwarequality.techtarget.com/definition/documentation
# A. (2012, December 03). What exactly is a startup? Retrieved November 14, 2017, from https://www.investopedia.com/ask/answers/12/what-is-a-startup.asp
# What is bytecode? - Definition from WhatIs.com. (n.d.). Retrieved November 14, 2017, from http://whatis.techtarget.com/definition/bytecode
# What is Ajax and Where is it Used in Technology? (2017, August 14). Retrieved November 14, 2017, from https://www.seguetech.com/ajax-technology/
# What is a web server? (n.d.). Retrieved November 14, 2017, from https://developer.mozilla.org/en-US/docs/Learn/Common_questions/What_is_a_web_server
# V. (n.d.). Interpreter. Retrieved November 14, 2017, from https://www.webopedia.com/TERM/I/interpreter.htmlFontinelle
# What is LISP (list processing)? - Definition from WhatIs.com. (n.d.). Retrieved November 14, 2017, from http://searchmicroservices.techtarget.com/definition/LISP-list-processing
# About. (n.d.). Retrieved November 14, 2017, from https://www.postgresql.org/about/Beal

{{Template: Cazinge}}

Cazinge Week 11

2017-11-14T08:00:01Z

Cazinge: /* References */ added link

=Presentation Prep=

==Unknown Terms==
* RFC 5321
** A specification of the basic protocol for Internet electronic mail transport. (1)
* plug-in
** A software component that adds a specific feature to an existing computer program. (2)
* documentation
** The information that describes the product to its users, consisting of product technical manuals and online information. (3)
* startups
** A young company that is just beginning to develop, usually small and initially financed and operated by a handful of founders or one individual.(4)
* bytecode
** Computer object code that is processed by a program, which converts each generalized machine instruction into a specific machine instruction that the computer's processor will understand. (5)
* Ajax
** A client-side script that communicates to and from a server/database without the need for a complete page refresh.(6)
* Web server
** A computer that is connected to the Internet, can be accessed through a domain name, and stores a website's component files and delivers them to the end-user's device. (7)
* Interpreter
** A program that executes instructions written in a high-level language. (8)
* Lisp
** A programming language that was designed for easy manipulation of data strings.(9)
* PostgreSQL
** An open source object-relational database system.(10)

==Article Outline==
* Software Development encompasses more than just the end-product itself
** Software development often encompasses more than just building a spec as previously defined.
** In order to properly carry out their tasks, Software developers need to understand the projects that they're building.
*** For example, Data can be stored, retrieved, accessed, and queried in a language similar to one that humans are generally comfortable with via SQL.
*** Creating schemas that encompass the use-cases of these specifications in a simple/effective manner allows one to create large applications such as Amazon. (5.2)
*** Understanding these projects requires a large repertoire of skills and languages known.
** Software development projects require communication in order to be conducted efficiently.
*** By having a baseline knowledge in the field of software engineering, one can have meaningful conversations like the one held between programmers A, B, and C. (5.1)
** In order to work effectively and build efficient projects, developers need to account for and understand all potential edge cases that could cause their programs to malfunction.
** Portable and extensible languages are often the most popular
*** Java gained the majority of its popularity due to its modularity, extensibility, platform support, and universal presence.
**** Multiple languages and projects were able to be developed with Java as a backbone
***** Jython
***** Jruby
***** Scala
***** Clojure
**** Research students, open source contributors, and hobbyists all contributed to the Java ecosystem indirectly, which benefit the community at large indirectly.
*** Javascript became the language that Java had originally aimed to be with it's applets
**** Extensible NPM package system lent itself towards open source projects, shared code, and universal adoption
**** Optimizations from Google resulted in the V8 engine, making Javascript a practical language for high speed computations.
**** Node.js enabled Javascript as a universal language, covering both general application logic, front-end, and back-end altogether. (5.7)
* Why does this matter
** Ford's article educates proper software development best practices through context and historical rather than
* What aspects of this work will inform how you carry out your final project?

==Executive Summary==
[[White Chicks|Week 11 (Abridged Summary)]]:
* As soon as I saw the assignment that my assigned group was given for our contribution to the GRNsight Gene Page assignment I began work on it, as I realized that the scope of the project could be accomplished somewhat succinctly with a proper approach to developing the specific functionality. The first thing that I did was divide the function into 4 helper functions, one dictionary mapping to facilitate retrieving the data, and a final function that was left unimplemented for future filtering and transformations of the data sources. After that, John and I went through the class' documented methods for reaching the final outcome of generating the gene page for a given gene symbol for each of the 4 databases. I was in charge of realizing 3 of them, as well as the scaffolding for the rest of the function, while John handled the last API in order to gain familiarity with the paradigm and working on a project of this scale. Due to my prior understanding of the technology we would use to create this function, I was successfully able to complete each of the 3 sub-functions that I was assigned, enabling users to gain data about any gene across the 3 of the 4 databases that we were assigned to work on. We have yet to interact with the JASPAR API team, but believe that the content left for the core assignment shouldn't be too high maintenance to finish from here. Outside of that, John and I worked collaboratively on the Journal Club presentations via our individual journals and by meeting together outside of class.

==Presentation Link==

https://xmlpipedb.cs.lmu.edu/biodb/fall2017/index.php/File:JLopezEAzingeJournalClub.pptx

==Acknowledgements==

I met with my homework partner John Lopez outside of class and worked on the presentation together. While I worked with the person noted above, this individual user entry was completed by me and not copied from another source.

[[User:Cazinge|Cazinge]] ([[User talk:Cazinge|talk]]) 23:59, 13 November 2017 (PST)

==References==

LMU BioDB 2017. (2017). Week 11. Retrieved November 10, 2017, from https://xmlpipedb.cs.lmu.edu/biodb/fall2017/index.php/Week_11

===Unknown Terms References===
# Simple Mail Transfer Protocol. (n.d.). Retrieved November 14, 2017, from https://tools.ietf.org/html/rfc5321.html
# Plug-in (computing). (2017, November 08). Retrieved November 14, 2017, from https://en.wikipedia.org/wiki/Plug-in_(computing)
# What is documentation? - Definition from WhatIs.com. (n.d.). Retrieved November 14, 2017, from http://searchsoftwarequality.techtarget.com/definition/documentation
# A. (2012, December 03). What exactly is a startup? Retrieved November 14, 2017, from https://www.investopedia.com/ask/answers/12/what-is-a-startup.asp
# What is bytecode? - Definition from WhatIs.com. (n.d.). Retrieved November 14, 2017, from http://whatis.techtarget.com/definition/bytecode
# What is Ajax and Where is it Used in Technology? (2017, August 14). Retrieved November 14, 2017, from https://www.seguetech.com/ajax-technology/
# What is a web server? (n.d.). Retrieved November 14, 2017, from https://developer.mozilla.org/en-US/docs/Learn/Common_questions/What_is_a_web_server
# V. (n.d.). Interpreter. Retrieved November 14, 2017, from https://www.webopedia.com/TERM/I/interpreter.htmlFontinelle
# What is LISP (list processing)? - Definition from WhatIs.com. (n.d.). Retrieved November 14, 2017, from http://searchmicroservices.techtarget.com/definition/LISP-list-processing
# About. (n.d.). Retrieved November 14, 2017, from https://www.postgresql.org/about/Beal

{{Template: Cazinge}}

Cazinge Week 11

2017-11-14T07:59:23Z

Cazinge: /* Acknowledgements */ adding acknowledgement

=Presentation Prep=

==Unknown Terms==
* RFC 5321
** A specification of the basic protocol for Internet electronic mail transport. (1)
* plug-in
** A software component that adds a specific feature to an existing computer program. (2)
* documentation
** The information that describes the product to its users, consisting of product technical manuals and online information. (3)
* startups
** A young company that is just beginning to develop, usually small and initially financed and operated by a handful of founders or one individual.(4)
* bytecode
** Computer object code that is processed by a program, which converts each generalized machine instruction into a specific machine instruction that the computer's processor will understand. (5)
* Ajax
** A client-side script that communicates to and from a server/database without the need for a complete page refresh.(6)
* Web server
** A computer that is connected to the Internet, can be accessed through a domain name, and stores a website's component files and delivers them to the end-user's device. (7)
* Interpreter
** A program that executes instructions written in a high-level language. (8)
* Lisp
** A programming language that was designed for easy manipulation of data strings.(9)
* PostgreSQL
** An open source object-relational database system.(10)

==Article Outline==
* Software Development encompasses more than just the end-product itself
** Software development often encompasses more than just building a spec as previously defined.
** In order to properly carry out their tasks, Software developers need to understand the projects that they're building.
*** For example, Data can be stored, retrieved, accessed, and queried in a language similar to one that humans are generally comfortable with via SQL.
*** Creating schemas that encompass the use-cases of these specifications in a simple/effective manner allows one to create large applications such as Amazon. (5.2)
*** Understanding these projects requires a large repertoire of skills and languages known.
** Software development projects require communication in order to be conducted efficiently.
*** By having a baseline knowledge in the field of software engineering, one can have meaningful conversations like the one held between programmers A, B, and C. (5.1)
** In order to work effectively and build efficient projects, developers need to account for and understand all potential edge cases that could cause their programs to malfunction.
** Portable and extensible languages are often the most popular
*** Java gained the majority of its popularity due to its modularity, extensibility, platform support, and universal presence.
**** Multiple languages and projects were able to be developed with Java as a backbone
***** Jython
***** Jruby
***** Scala
***** Clojure
**** Research students, open source contributors, and hobbyists all contributed to the Java ecosystem indirectly, which benefit the community at large indirectly.
*** Javascript became the language that Java had originally aimed to be with it's applets
**** Extensible NPM package system lent itself towards open source projects, shared code, and universal adoption
**** Optimizations from Google resulted in the V8 engine, making Javascript a practical language for high speed computations.
**** Node.js enabled Javascript as a universal language, covering both general application logic, front-end, and back-end altogether. (5.7)
* Why does this matter
** Ford's article educates proper software development best practices through context and historical rather than
* What aspects of this work will inform how you carry out your final project?

==Executive Summary==
[[White Chicks|Week 11 (Abridged Summary)]]:
* As soon as I saw the assignment that my assigned group was given for our contribution to the GRNsight Gene Page assignment I began work on it, as I realized that the scope of the project could be accomplished somewhat succinctly with a proper approach to developing the specific functionality. The first thing that I did was divide the function into 4 helper functions, one dictionary mapping to facilitate retrieving the data, and a final function that was left unimplemented for future filtering and transformations of the data sources. After that, John and I went through the class' documented methods for reaching the final outcome of generating the gene page for a given gene symbol for each of the 4 databases. I was in charge of realizing 3 of them, as well as the scaffolding for the rest of the function, while John handled the last API in order to gain familiarity with the paradigm and working on a project of this scale. Due to my prior understanding of the technology we would use to create this function, I was successfully able to complete each of the 3 sub-functions that I was assigned, enabling users to gain data about any gene across the 3 of the 4 databases that we were assigned to work on. We have yet to interact with the JASPAR API team, but believe that the content left for the core assignment shouldn't be too high maintenance to finish from here. Outside of that, John and I worked collaboratively on the Journal Club presentations via our individual journals and by meeting together outside of class.

==Presentation Link==

https://xmlpipedb.cs.lmu.edu/biodb/fall2017/index.php/File:JLopezEAzingeJournalClub.pptx

==Acknowledgements==

I met with my homework partner John Lopez outside of class and worked on the presentation together. While I worked with the person noted above, this individual user entry was completed by me and not copied from another source.

[[User:Cazinge|Cazinge]] ([[User talk:Cazinge|talk]]) 23:59, 13 November 2017 (PST)

==References==

===Unknown Terms References===
# Simple Mail Transfer Protocol. (n.d.). Retrieved November 14, 2017, from https://tools.ietf.org/html/rfc5321.html
# Plug-in (computing). (2017, November 08). Retrieved November 14, 2017, from https://en.wikipedia.org/wiki/Plug-in_(computing)
# What is documentation? - Definition from WhatIs.com. (n.d.). Retrieved November 14, 2017, from http://searchsoftwarequality.techtarget.com/definition/documentation
# A. (2012, December 03). What exactly is a startup? Retrieved November 14, 2017, from https://www.investopedia.com/ask/answers/12/what-is-a-startup.asp
# What is bytecode? - Definition from WhatIs.com. (n.d.). Retrieved November 14, 2017, from http://whatis.techtarget.com/definition/bytecode
# What is Ajax and Where is it Used in Technology? (2017, August 14). Retrieved November 14, 2017, from https://www.seguetech.com/ajax-technology/
# What is a web server? (n.d.). Retrieved November 14, 2017, from https://developer.mozilla.org/en-US/docs/Learn/Common_questions/What_is_a_web_server
# V. (n.d.). Interpreter. Retrieved November 14, 2017, from https://www.webopedia.com/TERM/I/interpreter.htmlFontinelle
# What is LISP (list processing)? - Definition from WhatIs.com. (n.d.). Retrieved November 14, 2017, from http://searchmicroservices.techtarget.com/definition/LISP-list-processing
# About. (n.d.). Retrieved November 14, 2017, from https://www.postgresql.org/about/Beal

{{Template: Cazinge}}

Cazinge Week 11

2017-11-14T07:57:27Z

Cazinge: /* Article Outline */ intermediary save

=Presentation Prep=

==Unknown Terms==
* RFC 5321
** A specification of the basic protocol for Internet electronic mail transport. (1)
* plug-in
** A software component that adds a specific feature to an existing computer program. (2)
* documentation
** The information that describes the product to its users, consisting of product technical manuals and online information. (3)
* startups
** A young company that is just beginning to develop, usually small and initially financed and operated by a handful of founders or one individual.(4)
* bytecode
** Computer object code that is processed by a program, which converts each generalized machine instruction into a specific machine instruction that the computer's processor will understand. (5)
* Ajax
** A client-side script that communicates to and from a server/database without the need for a complete page refresh.(6)
* Web server
** A computer that is connected to the Internet, can be accessed through a domain name, and stores a website's component files and delivers them to the end-user's device. (7)
* Interpreter
** A program that executes instructions written in a high-level language. (8)
* Lisp
** A programming language that was designed for easy manipulation of data strings.(9)
* PostgreSQL
** An open source object-relational database system.(10)

==Article Outline==
* Software Development encompasses more than just the end-product itself
** Software development often encompasses more than just building a spec as previously defined.
** In order to properly carry out their tasks, Software developers need to understand the projects that they're building.
*** For example, Data can be stored, retrieved, accessed, and queried in a language similar to one that humans are generally comfortable with via SQL.
*** Creating schemas that encompass the use-cases of these specifications in a simple/effective manner allows one to create large applications such as Amazon. (5.2)
*** Understanding these projects requires a large repertoire of skills and languages known.
** Software development projects require communication in order to be conducted efficiently.
*** By having a baseline knowledge in the field of software engineering, one can have meaningful conversations like the one held between programmers A, B, and C. (5.1)
** In order to work effectively and build efficient projects, developers need to account for and understand all potential edge cases that could cause their programs to malfunction.
** Portable and extensible languages are often the most popular
*** Java gained the majority of its popularity due to its modularity, extensibility, platform support, and universal presence.
**** Multiple languages and projects were able to be developed with Java as a backbone
***** Jython
***** Jruby
***** Scala
***** Clojure
**** Research students, open source contributors, and hobbyists all contributed to the Java ecosystem indirectly, which benefit the community at large indirectly.
*** Javascript became the language that Java had originally aimed to be with it's applets
**** Extensible NPM package system lent itself towards open source projects, shared code, and universal adoption
**** Optimizations from Google resulted in the V8 engine, making Javascript a practical language for high speed computations.
**** Node.js enabled Javascript as a universal language, covering both general application logic, front-end, and back-end altogether. (5.7)
* Why does this matter
** Ford's article educates proper software development best practices through context and historical rather than
* What aspects of this work will inform how you carry out your final project?

==Executive Summary==
[[White Chicks|Week 11 (Abridged Summary)]]:
* As soon as I saw the assignment that my assigned group was given for our contribution to the GRNsight Gene Page assignment I began work on it, as I realized that the scope of the project could be accomplished somewhat succinctly with a proper approach to developing the specific functionality. The first thing that I did was divide the function into 4 helper functions, one dictionary mapping to facilitate retrieving the data, and a final function that was left unimplemented for future filtering and transformations of the data sources. After that, John and I went through the class' documented methods for reaching the final outcome of generating the gene page for a given gene symbol for each of the 4 databases. I was in charge of realizing 3 of them, as well as the scaffolding for the rest of the function, while John handled the last API in order to gain familiarity with the paradigm and working on a project of this scale. Due to my prior understanding of the technology we would use to create this function, I was successfully able to complete each of the 3 sub-functions that I was assigned, enabling users to gain data about any gene across the 3 of the 4 databases that we were assigned to work on. We have yet to interact with the JASPAR API team, but believe that the content left for the core assignment shouldn't be too high maintenance to finish from here. Outside of that, John and I worked collaboratively on the Journal Club presentations via our individual journals and by meeting together outside of class.

==Acknowledgements==

==References==

===Unknown Terms References===
# Simple Mail Transfer Protocol. (n.d.). Retrieved November 14, 2017, from https://tools.ietf.org/html/rfc5321.html
# Plug-in (computing). (2017, November 08). Retrieved November 14, 2017, from https://en.wikipedia.org/wiki/Plug-in_(computing)
# What is documentation? - Definition from WhatIs.com. (n.d.). Retrieved November 14, 2017, from http://searchsoftwarequality.techtarget.com/definition/documentation
# A. (2012, December 03). What exactly is a startup? Retrieved November 14, 2017, from https://www.investopedia.com/ask/answers/12/what-is-a-startup.asp
# What is bytecode? - Definition from WhatIs.com. (n.d.). Retrieved November 14, 2017, from http://whatis.techtarget.com/definition/bytecode
# What is Ajax and Where is it Used in Technology? (2017, August 14). Retrieved November 14, 2017, from https://www.seguetech.com/ajax-technology/
# What is a web server? (n.d.). Retrieved November 14, 2017, from https://developer.mozilla.org/en-US/docs/Learn/Common_questions/What_is_a_web_server
# V. (n.d.). Interpreter. Retrieved November 14, 2017, from https://www.webopedia.com/TERM/I/interpreter.htmlFontinelle
# What is LISP (list processing)? - Definition from WhatIs.com. (n.d.). Retrieved November 14, 2017, from http://searchmicroservices.techtarget.com/definition/LISP-list-processing
# About. (n.d.). Retrieved November 14, 2017, from https://www.postgresql.org/about/Beal

{{Template: Cazinge}}

Cazinge Week 11

2017-11-14T07:14:23Z

Cazinge: /* Article Outline */ Adding main message

=Presentation Prep=

==Unknown Terms==
* RFC 5321
** A specification of the basic protocol for Internet electronic mail transport. (1)
* plug-in
** A software component that adds a specific feature to an existing computer program. (2)
* documentation
** The information that describes the product to its users, consisting of product technical manuals and online information. (3)
* startups
** A young company that is just beginning to develop, usually small and initially financed and operated by a handful of founders or one individual.(4)
* bytecode
** Computer object code that is processed by a program, which converts each generalized machine instruction into a specific machine instruction that the computer's processor will understand. (5)
* Ajax
** A client-side script that communicates to and from a server/database without the need for a complete page refresh.(6)
* Web server
** A computer that is connected to the Internet, can be accessed through a domain name, and stores a website's component files and delivers them to the end-user's device. (7)
* Interpreter
** A program that executes instructions written in a high-level language. (8)
* Lisp
** A programming language that was designed for easy manipulation of data strings.(9)
* PostgreSQL
** An open source object-relational database system.(10)

==Article Outline==
* What is the main message of this work?
** Software development often encompasses more than just building a spec as previously defined.
** In order to properly carry out their tasks, Software developers need to understand the projects that they're building.
*** For example, Data can be stored, retrieved, accessed, and queried in a language similar to one that humans are generally comfortable with via SQL.
*** Creating schemas that encompass the use-cases of these specifications in a simple/effective manner allows one to create large applications such as Amazon. (5.2)
*** Understanding these projects requires a large repertoire of skills and languages known.
** Software development projects require communication in order to be conducted efficiently.
*** By having a baseline knowledge in the field of software engineering, one can have meaningful conversations like the one held between programmers A, B, and C. (5.1)
** In order to work effectively and build efficient projects, developers need to account for and understand all potential edge cases that could cause their programs to malfunction.
** Portable and extensible languages are often the most popular
*** Java gained the majority of its popularity due to its modularity, extensibility, platform support, and universal presence.
**** Multiple languages and projects were able to be developed with Java as a backbone
***** Jython
***** Jruby
***** Scala
***** Clojure
**** Research students, open source contributors, and hobbyists all contributed to the Java ecosystem indirectly, which benefit the community at large indirectly.
*** Javascript became the language that Java had originally aimed to be with it's applets
**** Extensible NPM package system lent itself towards open source projects, shared code, and universal adoption
**** Optimizations from Google resulted in the V8 engine, making Javascript a practical language for high speed computations.
**** Node.js enabled Javascript as a universal language, covering both general application logic, front-end, and back-end altogether. (5.7)
* What is the importance or significance of this work?
* What design/development practices, processes, techniques, methods, or approaches were described in this work?
* Briefly state how these activities benefit the endeavors of web design and/or software development.
* Summarize the main points of the work.
* What aspects of this work will inform how you carry out your final project?

==Executive Summary==
[[White Chicks|Week 11 (Abridged Summary)]]:
* As soon as I saw the assignment that my assigned group was given for our contribution to the GRNsight Gene Page assignment I began work on it, as I realized that the scope of the project could be accomplished somewhat succinctly with a proper approach to developing the specific functionality. The first thing that I did was divide the function into 4 helper functions, one dictionary mapping to facilitate retrieving the data, and a final function that was left unimplemented for future filtering and transformations of the data sources. After that, John and I went through the class' documented methods for reaching the final outcome of generating the gene page for a given gene symbol for each of the 4 databases. I was in charge of realizing 3 of them, as well as the scaffolding for the rest of the function, while John handled the last API in order to gain familiarity with the paradigm and working on a project of this scale. Due to my prior understanding of the technology we would use to create this function, I was successfully able to complete each of the 3 sub-functions that I was assigned, enabling users to gain data about any gene across the 3 of the 4 databases that we were assigned to work on. We have yet to interact with the JASPAR API team, but believe that the content left for the core assignment shouldn't be too high maintenance to finish from here. Outside of that, John and I worked collaboratively on the Journal Club presentations via our individual journals and by meeting together outside of class.

==Acknowledgements==

==References==

===Unknown Terms References===
# Simple Mail Transfer Protocol. (n.d.). Retrieved November 14, 2017, from https://tools.ietf.org/html/rfc5321.html
# Plug-in (computing). (2017, November 08). Retrieved November 14, 2017, from https://en.wikipedia.org/wiki/Plug-in_(computing)
# What is documentation? - Definition from WhatIs.com. (n.d.). Retrieved November 14, 2017, from http://searchsoftwarequality.techtarget.com/definition/documentation
# A. (2012, December 03). What exactly is a startup? Retrieved November 14, 2017, from https://www.investopedia.com/ask/answers/12/what-is-a-startup.asp
# What is bytecode? - Definition from WhatIs.com. (n.d.). Retrieved November 14, 2017, from http://whatis.techtarget.com/definition/bytecode
# What is Ajax and Where is it Used in Technology? (2017, August 14). Retrieved November 14, 2017, from https://www.seguetech.com/ajax-technology/
# What is a web server? (n.d.). Retrieved November 14, 2017, from https://developer.mozilla.org/en-US/docs/Learn/Common_questions/What_is_a_web_server
# V. (n.d.). Interpreter. Retrieved November 14, 2017, from https://www.webopedia.com/TERM/I/interpreter.htmlFontinelle
# What is LISP (list processing)? - Definition from WhatIs.com. (n.d.). Retrieved November 14, 2017, from http://searchmicroservices.techtarget.com/definition/LISP-list-processing
# About. (n.d.). Retrieved November 14, 2017, from https://www.postgresql.org/about/Beal

{{Template: Cazinge}}

Cazinge Week 11

2017-11-14T05:57:26Z

Cazinge: Formatting

Cazinge Week 11

2017-11-14T05:56:26Z

Cazinge: Formatting

==Presentation Prep==

===Unknown Terms===
* RFC 5321
** A specification of the basic protocol for Internet electronic mail transport. (1)
* plug-in
** A software component that adds a specific feature to an existing computer program. (2)
* documentation
** The information that describes the product to its users, consisting of product technical manuals and online information. (3)
* startups
** A young company that is just beginning to develop, usually small and initially financed and operated by a handful of founders or one individual.(4)
* bytecode
** Computer object code that is processed by a program, which converts each generalized machine instruction into a specific machine instruction that the computer's processor will understand. (5)
* Ajax
** A client-side script that communicates to and from a server/database without the need for a complete page refresh.(6)
* Web server
** A computer that is connected to the Internet, can be accessed through a domain name, and stores a website's component files and delivers them to the end-user's device. (7)
* Interpreter
** A program that executes instructions written in a high-level language. (8)
* Lisp
** A programming language that was designed for easy manipulation of data strings.(9)
* PostgreSQL
** An open source object-relational database system.(10)

===Article Outline===

===Executive Summary===
[[White Chicks|Week 11 (Abridged Summary)]]:
* As soon as I saw the assignment that my assigned group was given for our contribution to the GRNsight Gene Page assignment I began work on it, as I realized that the scope of the project could be accomplished somewhat succinctly with a proper approach to developing the specific functionality. The first thing that I did was divide the function into 4 helper functions, one dictionary mapping to facilitate retrieving the data, and a final function that was left unimplemented for future filtering and transformations of the data sources. After that, John and I went through the class' documented methods for reaching the final outcome of generating the gene page for a given gene symbol for each of the 4 databases. I was in charge of realizing 3 of them, as well as the scaffolding for the rest of the function, while John handled the last API in order to gain familiarity with the paradigm and working on a project of this scale. Due to my prior understanding of the technology we would use to create this function, I was successfully able to complete each of the 3 sub-functions that I was assigned, enabling users to gain data about any gene across the 3 of the 4 databases that we were assigned to work on. We have yet to interact with the JASPAR API team, but believe that the content left for the core assignment shouldn't be too high maintenance to finish from here. Outside of that, John and I worked collaboratively on the Journal Club presentations via our individual journals and by meeting together outside of class.

===Acknowledgements===

===References===

====Unknown Terms References====
# Simple Mail Transfer Protocol. (n.d.). Retrieved November 14, 2017, from https://tools.ietf.org/html/rfc5321.html
# Plug-in (computing). (2017, November 08). Retrieved November 14, 2017, from https://en.wikipedia.org/wiki/Plug-in_(computing)
# What is documentation? - Definition from WhatIs.com. (n.d.). Retrieved November 14, 2017, from http://searchsoftwarequality.techtarget.com/definition/documentation
# A. (2012, December 03). What exactly is a startup? Retrieved November 14, 2017, from https://www.investopedia.com/ask/answers/12/what-is-a-startup.asp
# What is bytecode? - Definition from WhatIs.com. (n.d.). Retrieved November 14, 2017, from http://whatis.techtarget.com/definition/bytecode
# What is Ajax and Where is it Used in Technology? (2017, August 14). Retrieved November 14, 2017, from https://www.seguetech.com/ajax-technology/
# What is a web server? (n.d.). Retrieved November 14, 2017, from https://developer.mozilla.org/en-US/docs/Learn/Common_questions/What_is_a_web_server
# V. (n.d.). Interpreter. Retrieved November 14, 2017, from https://www.webopedia.com/TERM/I/interpreter.htmlFontinelle
# What is LISP (list processing)? - Definition from WhatIs.com. (n.d.). Retrieved November 14, 2017, from http://searchmicroservices.techtarget.com/definition/LISP-list-processing
# About. (n.d.). Retrieved November 14, 2017, from https://www.postgresql.org/about/Beal

{{Template: Cazinge}}

Cazinge Week 11

2017-11-14T05:55:13Z

Cazinge: /* Unknown Terms References */ updated with proper references

==Presentation Prep==

===Unknown Terms===
* RFC 5321
** A specification of the basic protocol for Internet electronic mail transport. (1)
* plug-in
** A software component that adds a specific feature to an existing computer program. (2)
* documentation
** The information that describes the product to its users, consisting of product technical manuals and online information. (3)
* startups
** A young company that is just beginning to develop, usually small and initially financed and operated by a handful of founders or one individual.(4)
* bytecode
** Computer object code that is processed by a program, which converts each generalized machine instruction into a specific machine instruction that the computer's processor will understand. (5)
* Ajax
** A client-side script that communicates to and from a server/database without the need for a complete page refresh.(6)
* Web server
** A computer that is connected to the Internet, can be accessed through a domain name, and stores a website's component files and delivers them to the end-user's device. (7)
* Interpreter
** A program that executes instructions written in a high-level language. (8)
* Lisp
** A programming language that was designed for easy manipulation of data strings.(9)
* PostgreSQL
** An open source object-relational database system.(10)

===Article Outline===

===Executive Summary===
[[White Chicks|Week 11 (Abridged Summary)]]:
* As soon as I saw the assignment that my assigned group was given for our contribution to the GRNsight Gene Page assignment I began work on it, as I realized that the scope of the project could be accomplished somewhat succinctly with a proper approach to developing the specific functionality. The first thing that I did was divide the function into 4 helper functions, one dictionary mapping to facilitate retrieving the data, and a final function that was left unimplemented for future filtering and transformations of the data sources. After that, John and I went through the class' documented methods for reaching the final outcome of generating the gene page for a given gene symbol for each of the 4 databases. I was in charge of realizing 3 of them, as well as the scaffolding for the rest of the function, while John handled the last API in order to gain familiarity with the paradigm and working on a project of this scale. Due to my prior understanding of the technology we would use to create this function, I was successfully able to complete each of the 3 sub-functions that I was assigned, enabling users to gain data about any gene across the 3 of the 4 databases that we were assigned to work on. We have yet to interact with the JASPAR API team, but believe that the content left for the core assignment shouldn't be too high maintenance to finish from here. Outside of that, John and I worked collaboratively on the Journal Club presentations via our individual journals and by meeting together outside of class.

===Acknowledgements===

====Unknown Terms References====
# Simple Mail Transfer Protocol. (n.d.). Retrieved November 14, 2017, from https://tools.ietf.org/html/rfc5321.html
# Plug-in (computing). (2017, November 08). Retrieved November 14, 2017, from https://en.wikipedia.org/wiki/Plug-in_(computing)
# What is documentation? - Definition from WhatIs.com. (n.d.). Retrieved November 14, 2017, from http://searchsoftwarequality.techtarget.com/definition/documentation
# A. (2012, December 03). What exactly is a startup? Retrieved November 14, 2017, from https://www.investopedia.com/ask/answers/12/what-is-a-startup.asp
# What is bytecode? - Definition from WhatIs.com. (n.d.). Retrieved November 14, 2017, from http://whatis.techtarget.com/definition/bytecode
# What is Ajax and Where is it Used in Technology? (2017, August 14). Retrieved November 14, 2017, from https://www.seguetech.com/ajax-technology/
# What is a web server? (n.d.). Retrieved November 14, 2017, from https://developer.mozilla.org/en-US/docs/Learn/Common_questions/What_is_a_web_server
# V. (n.d.). Interpreter. Retrieved November 14, 2017, from https://www.webopedia.com/TERM/I/interpreter.htmlFontinelle
# What is LISP (list processing)? - Definition from WhatIs.com. (n.d.). Retrieved November 14, 2017, from http://searchmicroservices.techtarget.com/definition/LISP-list-processing
# About. (n.d.). Retrieved November 14, 2017, from https://www.postgresql.org/about/Beal

===References===

{{Template: Cazinge}}

Cazinge Week 11

2017-11-14T05:47:16Z

Cazinge: added definitions

==Presentation Prep==

===Unknown Terms===
* RFC 5321
** A specification of the basic protocol for Internet electronic mail transport. (1)
* plug-in
** A software component that adds a specific feature to an existing computer program. (2)
* documentation
** The information that describes the product to its users, consisting of product technical manuals and online information. (3)
* startups
** A young company that is just beginning to develop, usually small and initially financed and operated by a handful of founders or one individual.(4)
* bytecode
** Computer object code that is processed by a program, which converts each generalized machine instruction into a specific machine instruction that the computer's processor will understand. (5)
* Ajax
** A client-side script that communicates to and from a server/database without the need for a complete page refresh.(6)
* Web server
** A computer that is connected to the Internet, can be accessed through a domain name, and stores a website's component files and delivers them to the end-user's device. (7)
* Interpreter
** A program that executes instructions written in a high-level language. (8)
* Lisp
** A programming language that was designed for easy manipulation of data strings.(9)
* PostgreSQL
** An open source object-relational database system.(10)

===Article Outline===

===Executive Summary===
[[White Chicks|Week 11 (Abridged Summary)]]:
* As soon as I saw the assignment that my assigned group was given for our contribution to the GRNsight Gene Page assignment I began work on it, as I realized that the scope of the project could be accomplished somewhat succinctly with a proper approach to developing the specific functionality. The first thing that I did was divide the function into 4 helper functions, one dictionary mapping to facilitate retrieving the data, and a final function that was left unimplemented for future filtering and transformations of the data sources. After that, John and I went through the class' documented methods for reaching the final outcome of generating the gene page for a given gene symbol for each of the 4 databases. I was in charge of realizing 3 of them, as well as the scaffolding for the rest of the function, while John handled the last API in order to gain familiarity with the paradigm and working on a project of this scale. Due to my prior understanding of the technology we would use to create this function, I was successfully able to complete each of the 3 sub-functions that I was assigned, enabling users to gain data about any gene across the 3 of the 4 databases that we were assigned to work on. We have yet to interact with the JASPAR API team, but believe that the content left for the core assignment shouldn't be too high maintenance to finish from here. Outside of that, John and I worked collaboratively on the Journal Club presentations via our individual journals and by meeting together outside of class.

===Acknowledgements===

====Unknown Terms References====
# https://tools.ietf.org/html/rfc5321.html
# https://en.wikipedia.org/wiki/Plug-in_(computing)
# http://searchsoftwarequality.techtarget.com/definition/documentation
# https://www.investopedia.com/ask/answers/12/what-is-a-startup.asp
# http://whatis.techtarget.com/definition/bytecode
# https://www.seguetech.com/ajax-technology/
# https://developer.mozilla.org/en-US/docs/Learn/Common_questions/What_is_a_web_server
# https://www.webopedia.com/TERM/I/interpreter.html
# http://searchmicroservices.techtarget.com/definition/LISP-list-processing
# https://www.postgresql.org/about/

===References===

{{Template: Cazinge}}

Cazinge Week 11

2017-11-14T04:56:05Z

Cazinge: /* Acknowledgements */ adding first reference

==Presentation Prep==

===Unknown Terms===
* RFC 5321
** A specification of the basic protocol for Internet electronic mail transport.
* plug-in
**
* documentation
**
* startups
**
* bytecode
**
* Ajax
**
* Web server
**
* Interpreter
**
* Lisp
**
* PostgreSQL
**

===Article Outline===

===Executive Summary===
[[White Chicks|Week 11 (Abridged Summary)]]:
* As soon as I saw the assignment that my assigned group was given for our contribution to the GRNsight Gene Page assignment I began work on it, as I realized that the scope of the project could be accomplished somewhat succinctly with a proper approach to developing the specific functionality. The first thing that I did was divide the function into 4 helper functions, one dictionary mapping to facilitate retrieving the data, and a final function that was left unimplemented for future filtering and transformations of the data sources. After that, John and I went through the class' documented methods for reaching the final outcome of generating the gene page for a given gene symbol for each of the 4 databases. I was in charge of realizing 3 of them, as well as the scaffolding for the rest of the function, while John handled the last API in order to gain familiarity with the paradigm and working on a project of this scale. Due to my prior understanding of the technology we would use to create this function, I was successfully able to complete each of the 3 sub-functions that I was assigned, enabling users to gain data about any gene across the 3 of the 4 databases that we were assigned to work on. We have yet to interact with the JASPAR API team, but believe that the content left for the core assignment shouldn't be too high maintenance to finish from here. Outside of that, John and I worked collaboratively on the Journal Club presentations via our individual journals and by meeting together outside of class.

===Acknowledgements===

====Unknown Terms References====
# https://tools.ietf.org/html/rfc5321.html
#
#
#
#
#
#
#
#
#

===References===

{{Template: Cazinge}}

Cazinge Week 11

2017-11-14T04:52:36Z

Cazinge: /* Unknown Terms */ small edit

Cazinge Week 11

2017-11-14T03:14:59Z

Cazinge: /* Unknown Terms */ added description

==Presentation Prep==

===Unknown Terms===
* RFC 5321
** <ref name="RFC_5321">A specification of the basic protocol for Internet electronic mail transport.</ref>
* rathole
**
* documentation
**
* startups
**
* bytecode
**
* Ajax
**
* Web server
**
* Interpreter
**
* Lisp
**
* PostgreSQL
**

===Article Outline===

===Executive Summary===
[[White Chicks|Week 11 (Abridged Summary)]]:
* As soon as I saw the assignment that my assigned group was given for our contribution to the GRNsight Gene Page assignment I began work on it, as I realized that the scope of the project could be accomplished somewhat succinctly with a proper approach to developing the specific functionality. The first thing that I did was divide the function into 4 helper functions, one dictionary mapping to facilitate retrieving the data, and a final function that was left unimplemented for future filtering and transformations of the data sources. After that, John and I went through the class' documented methods for reaching the final outcome of generating the gene page for a given gene symbol for each of the 4 databases. I was in charge of realizing 3 of them, as well as the scaffolding for the rest of the function, while John handled the last API in order to gain familiarity with the paradigm and working on a project of this scale. Due to my prior understanding of the technology we would use to create this function, I was successfully able to complete each of the 3 sub-functions that I was assigned, enabling users to gain data about any gene across the 3 of the 4 databases that we were assigned to work on. We have yet to interact with the JASPAR API team, but believe that the content left for the core assignment shouldn't be too high maintenance to finish from here. Outside of that, John and I worked collaboratively on the Journal Club presentations via our individual journals and by meeting together outside of class.

===Acknowledgements===

===References===

{{Template: Cazinge}}

Cazinge Week 11

2017-11-14T03:08:12Z

Cazinge: /* Unknown Terms */ added terms

==Presentation Prep==

===Unknown Terms===
* RFC 5321 and RFC 5322
* rathole
* documentation
* startups
* bytecode
* Ajax
* Web server
* Interpreter
* Lisp
* PostgreSQL

===Article Outline===

===Executive Summary===
[[White Chicks|Week 11 (Abridged Summary)]]:
* As soon as I saw the assignment that my assigned group was given for our contribution to the GRNsight Gene Page assignment I began work on it, as I realized that the scope of the project could be accomplished somewhat succinctly with a proper approach to developing the specific functionality. The first thing that I did was divide the function into 4 helper functions, one dictionary mapping to facilitate retrieving the data, and a final function that was left unimplemented for future filtering and transformations of the data sources. After that, John and I went through the class' documented methods for reaching the final outcome of generating the gene page for a given gene symbol for each of the 4 databases. I was in charge of realizing 3 of them, as well as the scaffolding for the rest of the function, while John handled the last API in order to gain familiarity with the paradigm and working on a project of this scale. Due to my prior understanding of the technology we would use to create this function, I was successfully able to complete each of the 3 sub-functions that I was assigned, enabling users to gain data about any gene across the 3 of the 4 databases that we were assigned to work on. We have yet to interact with the JASPAR API team, but believe that the content left for the core assignment shouldn't be too high maintenance to finish from here. Outside of that, John and I worked collaboratively on the Journal Club presentations via our individual journals and by meeting together outside of class.

===Acknowledgements===

===References===

{{Template: Cazinge}}

Cazinge Week 11

2017-11-14T02:45:07Z

Cazinge: /* Executive Summary */ added description

==Presentation Prep==

===Unknown Terms===

===Article Outline===

===Executive Summary===
[[White Chicks|Week 11 (Abridged Summary)]]:
* As soon as I saw the assignment that my assigned group was given for our contribution to the GRNsight Gene Page assignment I began work on it, as I realized that the scope of the project could be accomplished somewhat succinctly with a proper approach to developing the specific functionality. The first thing that I did was divide the function into 4 helper functions, one dictionary mapping to facilitate retrieving the data, and a final function that was left unimplemented for future filtering and transformations of the data sources. After that, John and I went through the class' documented methods for reaching the final outcome of generating the gene page for a given gene symbol for each of the 4 databases. I was in charge of realizing 3 of them, as well as the scaffolding for the rest of the function, while John handled the last API in order to gain familiarity with the paradigm and working on a project of this scale. Due to my prior understanding of the technology we would use to create this function, I was successfully able to complete each of the 3 sub-functions that I was assigned, enabling users to gain data about any gene across the 3 of the 4 databases that we were assigned to work on. We have yet to interact with the JASPAR API team, but believe that the content left for the core assignment shouldn't be too high maintenance to finish from here. Outside of that, John and I worked collaboratively on the Journal Club presentations via our individual journals and by meeting together outside of class.

===Acknowledgements===

===References===

{{Template: Cazinge}}

Cazinge Week 11

2017-11-14T02:32:08Z

Cazinge: /* Executive Summary */ formatting

==Presentation Prep==

===Unknown Terms===

===Article Outline===

===Executive Summary===
[[White Chicks|Week 11 (Abridged Summary)]]:
* This week, I finished the bulk of the function logic for our final project; I also performed research on the items necessary for an effective journal club presentation this coming Thursday by completing the individual assignment that we were assigned for the week.
* Getting a large portion of our designated assignment completed early worked pretty successfully to alleviate our stress over the impending assignment and place us ahead of the pace of other groups so that we would be able to work on the more secondary parts of our final presentation across the next 4 weeks.
* Working on the project without having a proper development environment set up ended up detracting from the fluidity of our collaboration as a team; as the versions of the final function that John and I worked on weren't linked via git, and were somewhat unruly to collaborate on.
* Next time, I'll make sure to set up my development environment according to Dondi's instructions on the Coders Guild Page.

===Acknowledgements===

===References===

{{Template: Cazinge}}

Cazinge Week 11

2017-11-14T02:31:45Z

Cazinge: added summary

White Chicks

2017-11-14T02:30:06Z

Cazinge: /* Executive Summaries */ added summary for week

==White Chicks==

===General Information===
Eddie (Cazinge):
*Role: Coder
*User Page: [[User:cazinge|Eddie Azinge]]

Dina (Dbashour):
*Role: Data Analysis
*User Page: [[User:dbashour|Dina Bashoura]]

Corinne (Cwong34):
*Role: Project Manager/Quality Assurance
*User Page: [[User:Cwong34|Corinne Wong]]

John (johnllopez616):
*Role: Coder
*User Page: [[User:johnllopez616|John Lopez]]

===Executive Summaries===
'''Eddie''':
* [[Cazinge Week 11|Week 11]]:
** This week, I finished the bulk of the function logic for our final project; I also performed research on the items necessary for an effective journal club presentation this coming Thursday by completing the individual assignment that we were assigned for the week.
** Getting a large portion of our designated assignment completed early worked pretty successfully to alleviate our stress over the impending assignment and place us ahead of the pace of other groups so that we would be able to work on the more secondary parts of our final presentation across the next 4 weeks.
** Working on the project without having a proper development environment set up ended up detracting from the fluidity of our collaboration as a team; as the versions of the final function that John and I worked on weren't linked via git, and were somewhat unruly to collaborate on.
** Next time, I'll make sure to set up my development environment according to Dondi's instructions on the Coders Guild Page.

'''Dina''':
* Week 11:
**

'''Corinne''':
* Week 11:
**

'''John''':
* Week 11:
**

{{template:White_Chicks}}

White Chicks

2017-11-14T02:14:20Z

Cazinge: /* Executive Summaries */ added link

Template:Cazinge

2017-11-14T02:13:14Z

Cazinge: /* My Links */ Added week 11

Cazinge Week 11

2017-11-14T02:10:46Z

Cazinge: Templating page

==Presentation Prep==

===Unknown Terms===

===Article Outline===

===Acknowledgements===

===References===

{{Template: Cazinge}}

White Chicks

2017-11-14T02:04:16Z

Cazinge: Formatting

Week 11

2017-11-08T01:27:37Z

Cazinge: /* Individual Journal Assignment */ bumped week number

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

== Overview ==

The objectives of this week's exercise is based on your assigned role on your team:
* Everyone will contribute to [[Week_11#Team_Journal_Assignment | Creating your team's home page,]] and in the process, getting yourselves organized for the final project.
* Coders (and Designer) will then move on to preparing a journal club presentation for Tuesday, November 14 on their assigned papers.
* QA's and Data Analysts will search the literature and microarray databases for additional papers on the transcriptional response to cold shock in yeast. (They will prepare their journal club presentations on one of these papers for the [[Week 12]] assignment and deliver the presentation itself on Tuesday, November 21.)

=== 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 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 (whether delivered on November 14 or 21) will be worth a total of 40 points.

== 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.
* For your assignment this week, '''''electronic laboratory notebook''''' will be modified to fit the assignment that is specific to your role on your team.

=== Team Membership ===

The project groups and roles are are:
# Page Design
#* Project Manager/Quality Assurance: Hayden
#* Data Analysis: Mary
#* Coder: Arash
#* Designer: Nicole
# Gene Database APIs
#* Project Manager/Quality Assurance: Corinne
#* Data Analysis: Dina
#* Coders: Eddie A. and John
# JASPAR API
#* Project Manager/Quality Assurance: Quinn
#* Data Analysis: Antonio
#* Coders: Eddie B. and Simon
# Interaction and Integration
#* Project Manager/Quality Assurance: Katie
#* Data Analysis: Emma
#* Coders: Blair and Zach

=== '''Coders and Designer''': Prepare a Journal Club Presentation for Your Assigned Paper ===

Your team will split into two halves for journal club presentations that will take place in class on Tuesday, November 14 and Tuesday, November 21. The Coders (and Designer) will present first on November 14, while the QA's and Data Analysts will present second on November 21.

==== Paper Assignments ====
* '''Page Design team:''' Liikkanen, L. A. (2017). The data-driven design era in professional web design. ''interactions'', ''24''(5), 52-57. [https://doi.org/10.1145/3121355 DOI: 10.1145/3121355]
* '''Gene Database APIs team:''' [https://www.bloomberg.com/graphics/2015-paul-ford-what-is-code/#the-time-you-attended-the-e-mail-address-validation-meeting Ford, P. (2015). What is Code? Part 5: The Time You Attended the E-mail Address Validation Meeting. ''Bloomberg Businessweek'' (June 11, 2015).]
* '''JASPAR API team:''' [https://www.bloomberg.com/graphics/2015-paul-ford-what-is-code/#how-are-apps-made Ford, P. (2015). What is Code? Part 6: How Are Apps Made? ''Bloomberg Businessweek'' (June 11, 2015).]
* '''Interaction and Integration team:''' [https://www.theatlantic.com/technology/archive/2015/07/the-secret-startup-saved-healthcare-gov-the-worst-website-in-america/397784/ Meyer, R. (2015). The secret startup that saved the worst website in America. ''The Atlantic'' (July 9, 2015).]

==== 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 design or development terms (e.g., techniques, processes, technologies, architectures, environments, etc.) whose definitions you did not know when you first read the article. Define each of the terms. You can use any reputable technology reference source (e.g., [http://developer.mozilla.org Mozilla Developer Network], [https://www.programmableweb.com Programmable Web], etc.) as a source for definitions. If you don’t know where to look, you may look up the term in Wikipedia but ''select a primary or secondary source'' from the Wikipedia article’s references as your actual source. If a technology has an official or definitive website (e.g., https://nodejs.org for Node), cite the '''About''' page within that site for that technology. Cite these definition sources by providing an in-text citation that corresponds to an entry in your References section. Use APA formatting and provide a hyperlink to the URL if it is a web citation. '''''Each definition must have its own citation, even if you used the same overall source.'''''
# Write an outline of the article. The length should be a minimum of the equivalent of 2 pages of standard 8 1/2 by 11 inch paper (you can use the "Print Preview" option in your browser at 100% scale to see 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.
#* What is the main message of this work?
#* What is the importance or significance of this work?
#* What design/development practices, processes, techniques, methods, or approaches were described in this work?
#* Briefly state how these activities benefit the endeavors of web design and/or software development.
#* Summarize the main points of the work.
#* What aspects of this work will inform how you carry out your final project?

==== Journal Club Presentation ====

The Coders (and Designer) will prepare and give a 15-minute PowerPoint presentation for their paper in class on Tuesday, November 14.
* Please follow the [[Media:PresentationGuidelines.ppt | Presentation Guidelines]] for how to format your slides.
* You will need to prepare ~15 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.
* ''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, November 14.'''''
** 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 evalutated 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 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, we expect that you will take the feedback from your previous presentation into account when doing this one.

=== '''QA's and Data Analysts:''' Annotated Bibliography of Papers that Report Microarray Data from Yeast Subjected to Cold Shock ===

This exercise will be primarily performed in class on Thursday, November 9. The purpose of this exercise is to create a bibliography of papers that report microarray data from yeast subjected to cold shock. You will present one of the primary research articles you find as a journal club presentation on Tuesday, November 21. You will also use these articles to write the Introduction and Discussion sections of your final group report.

# Create a bibliography of a minimum of 4 citations to primary research articles (2 contributed by the Data Analyst and 2 contributed by the QA) that report microarray data from yeast subjected to cold shock.
#* Each of the 4 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 http://libguides.lmu.edu/content.php?pid=25618&sid=184708. You will be using one of three formats, journal article from database (with DOI), journal article from database (no DOI) or journal article in print (no DOI).)
#** The link to the abstract from PubMed.
#** The link to the full text of the article in PubMedCentral.
#** 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 and what is the availability?
#*** Does the journal or the authors own the copyright?
#*** Is the article available “Open Access” upon publication under a Creative Commons license?
#*** If the article is not Open Access, is it available for free after a certain period of time has elapsed?
#** What organization is the publisher of the article?
#*** What type of organization is it? (commercial, for-profit publisher, scientific society, respected open access organization like Public Library of Science or BioMedCentral, or predatory open access organization; see http://oaspa.org/membership/members/ for a list of members of the Open Access Scholarly Publishers Association)
#** Is this article available in print or online only?
#*** Has LMU paid a subscription or other fee for your access to this 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 the following three review articles in Web of Science and answer the following:
#*** Aguilera, J., Randez-Gil, F., & Prieto, J.A. (2007). Cold Response in Saccharomyces cerevisiae: New Functions for Old Mechanisms. FEMS Microbiological Reviews, 31, 327–341. doi: 10.1111/j.1574-6976.2007.00066.x
#**** How many articles does this article cite?
#**** How many articles cite this article?
#*** Al-Fageeh, M.B. & Smales, C.M. (2006). Control and Regulation of the Cellular Responses to Cold Shock: the Responses in Yeast and Mammalian Systems. Biochemical Journal, 397, 247–259. doi: 10.1042/BJ20060166
#**** How many articles does this article cite?
#**** How many articles cite this article?
#*** Thieringer, H.A., Jones, P.G.,& Inouye, M. (1998). Cold shock and adaptation. BioEssays, 20, 49–57. doi: 10.1002/(SICI)1521-1878(199801)20:1<49::AID-BIES8>3.0.CO;2-N
#**** How many articles does this article cite?
#**** How many articles cite this article?

==== 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
* Publisher: American Society for Cell Biology (scientific society)
* Availability: in print and online
* Did LMU pay a fee for this article: no

== '''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.
# 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:Assignment]]
[[Category:Group Projects]]

White Chicks

2017-11-08T01:23:04Z

Cazinge: removed improper table

====White Chicks====

Eddie's Contributions:
*Role: Coder
*User Page: [[User:cazinge|Eddie Azinge]]

Dina's Contributions:
*Role: Data Analysis
*User Page: [[User:dbashour|Dina Bashoura]]

Corinne's Contributions:
*Role: Project Manager/Quality Assurance
*User Page: [[User:Cwong34|Corinne Wong]]

John's Contributions:
*Role: Coder
*User Page: [[User:johnllopez616|John Lopez]]

{{template:White_Chicks}}

File:Wt p-value slide EA.pptx

2017-11-07T00:09:38Z

Cazinge: Cazinge uploaded a new version of File:Wt p-value slide EA.pptx

Class Journal Week 10

2017-11-06T23:52:21Z

Cazinge: /* Eddie Azinge's Responses */

=Class Journal Week 10=
==Hayden Hinsch's Responses==

{{hhinsch}}

==Mary Balducci's Responses==
#I would want for my teammates to be available if I have questions about the work, and also to feel like they can ask me questions. I would want us all to share equal amounts of the work, and to be able to get in touch with each other if we need help with something.
#I think communication makes teamwork go smoothly. If we are all able to tell each other what we're working on and how we're doing it, I think it helps everyone better understand the project being done. This also includes being responsive to questions and figuring out who is responsible for each aspect of the project together.
#I think teamwork does not go smoothly when members of the team are not responsive. I think it's harder when I'm not entirely sure what another person in my group is doing, or if I have a question and they are not available to answer it.

[[User:Mbalducc|Mbalducc]] ([[User talk:Mbalducc|talk]]) 07:44, 3 November 2017 (PDT)

==Zachary Van Ysseldyk's Responses==
#As far as characteristics for a teammate, I value those who do not procrastinate and plan out how the assignment will get done to avoid unnecessary stress. Stringing along the same thought, I think that communication is crucial so that every person on the team is on the same page. I would also value those who assume the same amount of work as everybody else so that we all get a fair and well deserved grade.
#Bleeding in from question 1, I think that solid communication and a laid out plan will help the project go smoothly. Knowing when things will get done and how long they will take will help for smooth project completion.
#On the other hand, poor communication will greatly negatively affect a project. People might be doing the same things, people might not understand what they are doing which leads to frustration and division among the team. Also if somebody does not put in a sufficient amount of effort, this will anger others as the person who didn't do the the work should not receive the same letter grade.
 
[[User:Zvanysse|Zvanysse]] ([[User talk:Zvanysse|talk]]) 13:36, 5 November 2017 (PST)
 
{{Template:Zvanysse}}

==Dina Bashoura's Responses==
#I would like a partner who is well equipped in computer science, since I am lagging in that department. I also would like my partner to be good at communicating so that we can distinguish who should do what task and work together to make the overall project flow.
# Things like communication, trust, reliability, and good team chemistry make teamwork go smoothly.
#Things like lack of communication, doing sections not assigned to you or not assigning sections at all, not showing up to group meetings, and not being prepared when group meetings take place are things that make teamwork fail.
[[User:Dbashour|Dbashour]] ([[User talk:Dbashour|talk]]) 14:45, 6 November 2017 (PST)
{{template:dbashour}}

==Eddie Azinge's Responses==
# I don't find myself requiring very much from my teammates, just that the work that they hold themselves accountable for gets done at some point or another, and that inconsistencies in their work do not end up propogating upwards and affecting the status of the entire group, specifically because everyone has their own circumstances and priorities; as long as they deliver on their promises they're good in my book.
# Communication is key to teamwork; but trust and flexibility also play huge roles as well.
# Somewhat antithetical to the last question, a lack of trust and need to keep constant tabs on the members of the project often leads to rushed work, resentment, and dissidence within the team.
[[User:Cazinge|Cazinge]] ([[User talk:Cazinge|talk]]) 15:46, 6 November 2017 (PST)

[[Category:Shared]]
[[Category:Journal Entry]]

Class Journal Week 10

2017-11-06T23:46:45Z

Cazinge: Blocking out response

=Class Journal Week 10=
==Hayden Hinsch's Responses==

{{hhinsch}}

==Mary Balducci's Responses==
#I would want for my teammates to be available if I have questions about the work, and also to feel like they can ask me questions. I would want us all to share equal amounts of the work, and to be able to get in touch with each other if we need help with something.
#I think communication makes teamwork go smoothly. If we are all able to tell each other what we're working on and how we're doing it, I think it helps everyone better understand the project being done. This also includes being responsive to questions and figuring out who is responsible for each aspect of the project together.
#I think teamwork does not go smoothly when members of the team are not responsive. I think it's harder when I'm not entirely sure what another person in my group is doing, or if I have a question and they are not available to answer it.

[[User:Mbalducc|Mbalducc]] ([[User talk:Mbalducc|talk]]) 07:44, 3 November 2017 (PDT)

==Zachary Van Ysseldyk's Responses==
#As far as characteristics for a teammate, I value those who do not procrastinate and plan out how the assignment will get done to avoid unnecessary stress. Stringing along the same thought, I think that communication is crucial so that every person on the team is on the same page. I would also value those who assume the same amount of work as everybody else so that we all get a fair and well deserved grade.
#Bleeding in from question 1, I think that solid communication and a laid out plan will help the project go smoothly. Knowing when things will get done and how long they will take will help for smooth project completion.
#On the other hand, poor communication will greatly negatively affect a project. People might be doing the same things, people might not understand what they are doing which leads to frustration and division among the team. Also if somebody does not put in a sufficient amount of effort, this will anger others as the person who didn't do the the work should not receive the same letter grade.
 
[[User:Zvanysse|Zvanysse]] ([[User talk:Zvanysse|talk]]) 13:36, 5 November 2017 (PST)
 
{{Template:Zvanysse}}

==Dina Bashoura's Responses==
#I would like a partner who is well equipped in computer science, since I am lagging in that department. I also would like my partner to be good at communicating so that we can distinguish who should do what task and work together to make the overall project flow.
# Things like communication, trust, reliability, and good team chemistry make teamwork go smoothly.
#Things like lack of communication, doing sections not assigned to you or not assigning sections at all, not showing up to group meetings, and not being prepared when group meetings take place are things that make teamwork fail.
[[User:Dbashour|Dbashour]] ([[User talk:Dbashour|talk]]) 14:45, 6 November 2017 (PST)
{{template:dbashour}}

==Eddie Azinge's Responses==
#
#
#
[[User:Cazinge|Cazinge]] ([[User talk:Cazinge|talk]]) 15:46, 6 November 2017 (PST)

[[Category:Shared]]
[[Category:Journal Entry]]

Cazinge Week 9

2017-11-06T23:44:11Z

Cazinge: /* Acknowledgements */ adding signiture

==Part 1: GRNsight Testing==

[https://github.com/dondi/GRNsight/blob/beta/test-files/demo-files/21-genes_50-edges_Dahlquist-data_estimation_output.xlsx XLSX file]

[https://github.com/dondi/GRNsight/blob/beta/test-files/demo-files/21-genes_31-edges_Schade-data_input.sif SIF file]

[https://github.com/dondi/GRNsight/blob/beta/test-files/demo-files/21-genes_31-edges_Schade-data_estimation_output_unweighted.graphml GRAPHML file]

===GRNsight Client Side Testing Document: D-Pad Control===
Last Updated: 2017-10-23
; Test 1
: Instructions:
:* Load Graph - File Menu -> Open
: Results:
:* GRNsight should lay out a network graph from the Excel workbook if there are no errors in the file
: '''Conclusions:'''
:* GRNsight appears to successfully lay out the mentioned network graph as specified from the Excel workbook.
; Test 2
: Instructions:
:* Load Graph - File Menu -> Import SIF
: Results:
:* GRNsight should lay out a network graph from the SIF file if there are no errors in the file
: '''Conclusions:'''
:* GRNsight appears to successfully lay out the mentioned network graph as specified from the SIF file.
; Test 3
: Instructions:
:* Load Graph - File Menu -> Import GraphML
: Results:
:* GRNsight should lay out a network graph from the GraphML file if there are no errors in the file
: '''Conclusions:'''
:* GRNsight appears to successfully lay out the mentioned network graph as specified from the GraphML file.
; Test 4
: Instructions:
:* Load Graph - File Menu -> Open
:* D-Pad Control - Click Right Arrow
: Results:
:* GRNsight should lay out a network graph from the Excel workbook if there are no errors in the file
:* if "Restrict Graph to Viewport" is Unchecked., The graph should shift to the left
: '''Conclusions:'''
:* GRNsight appears to successfully shift to the left when the Excel workbook is loaded and restrict graph to viewport is unchecked.
; Test 5
: Instructions:
:* Load Graph - File Menu -> Import SIF
:* D-Pad Control - Click Right Arrow
: Results:
:* GRNsight should lay out a network graph from the SIF file if there are no errors in the file
:* if "Restrict Graph to Viewport" is Unchecked., The graph should shift to the left
: '''Conclusions:'''
:* GRNsight appears to successfully shift to the left when the SIF file is loaded and restrict graph to viewport is unchecked.
; Test 6
: Instructions:
:* Load Graph - File Menu -> Import GraphML
:* D-Pad Control - Click Right Arrow
: Results:
:* GRNsight should lay out a network graph from the GraphML file if there are no errors in the file
:* if "Restrict Graph to Viewport" is Unchecked., The graph should shift to the left
: '''Conclusions:'''
:* GRNsight appears to successfully shift to the left when the GraphML file is loaded and restrict graph to viewport is unchecked.
; Test 7
: Instructions:
:* Load Graph - File Menu -> Open
:* D-Pad Control - Click Left Arrow
: Results:
:* GRNsight should lay out a network graph from the Excel workbook if there are no errors in the file
:* if "Restrict Graph to Viewport" is Unchecked., The graph should shift to the right
: '''Conclusions:'''
:* GRNsight appears to successfully shift to the right when the Excel workbook is loaded and restrict graph to viewport is unchecked.
; Test 8
: Instructions:
:* Load Graph - File Menu -> Import SIF
:* D-Pad Control - Click Left Arrow
: Results:
:* GRNsight should lay out a network graph from the SIF file if there are no errors in the file
:* if "Restrict Graph to Viewport" is Unchecked., The graph should shift to the right
: '''Conclusions:'''
:* GRNsight appears to successfully shift to the right when the SIF file is loaded and restrict graph to viewport is unchecked.
; Test 9
: Instructions:
:* Load Graph - File Menu -> Import GraphML
:* D-Pad Control - Click Left Arrow
: Results:
:* GRNsight should lay out a network graph from the GraphML file if there are no errors in the file
:* if "Restrict Graph to Viewport" is Unchecked., The graph should shift to the right
: '''Conclusions:'''
:* GRNsight appears to successfully shift to the right when the GraphML file is loaded and restrict graph to viewport is unchecked.
; Test 10
: Instructions:
:* Load Graph - File Menu -> Open
:* D-Pad Control - Click Up Arrow
: Results:
:* GRNsight should lay out a network graph from the Excel workbook if there are no errors in the file
:* if "Restrict Graph to Viewport" is Unchecked., The graph should shift down
: '''Conclusions:'''
:* GRNsight appears to successfully shift down when the Excel workbook is loaded and restrict graph to viewport is unchecked.
; Test 11
: Instructions:
:* Load Graph - File Menu -> Import SIF
:* D-Pad Control - Click Up Arrow
: Results:
:* GRNsight should lay out a network graph from the SIF file if there are no errors in the file
:* if "Restrict Graph to Viewport" is Unchecked., The graph should shift down
: '''Conclusions:'''
:* GRNsight appears to successfully shift down when the SIF file is loaded and restrict graph to viewport is unchecked.
; Test 12
: Instructions:
:* Load Graph - File Menu -> Import GraphML
:* D-Pad Control - Click Up Arrow
: Results:
:* GRNsight should lay out a network graph from the GraphML file if there are no errors in the file
:* if "Restrict Graph to Viewport" is Unchecked., The graph should shift down
: '''Conclusions:'''
:* GRNsight appears to successfully shift down when the GraphML file is loaded and restrict graph to viewport is unchecked.
; Test 13
: Instructions:
:* Load Graph - File Menu -> Open
:* D-Pad Control - Click Down Arrow
: Results:
:* GRNsight should lay out a network graph from the Excel workbook if there are no errors in the file
:* if "Restrict Graph to Viewport" is Unchecked., The graph should shift up
: '''Conclusions:'''
:* GRNsight appears to successfully shift up when the Excel workbook is loaded and restrict graph to viewport is unchecked.
; Test 14
: Instructions:
:* Load Graph - File Menu -> Import SIF
:* D-Pad Control - Click Down Arrow
: Results:
:* GRNsight should lay out a network graph from the SIF file if there are no errors in the file
:* if "Restrict Graph to Viewport" is Unchecked., The graph should shift up
: '''Conclusions:'''
:* GRNsight appears to successfully shift up when the SIF file is loaded and restrict graph to viewport is unchecked.
; Test 15
: Instructions:
:* Load Graph - File Menu -> Import GraphML
:* D-Pad Control - Click Down Arrow
: Results:
:* GRNsight should lay out a network graph from the GraphML file if there are no errors in the file
:* if "Restrict Graph to Viewport" is Unchecked., The graph should shift up
: '''Conclusions:'''
:* GRNsight appears to successfully shift up when the GraphML file is loaded and restrict graph to viewport is unchecked.
; Test 16
: Instructions:
:* Load Graph - File Menu -> Open
:* D-Pad Control - Click Center Button
: Results:
:* GRNsight should lay out a network graph from the Excel workbook if there are no errors in the file
:* if "Restrict Graph to Viewport" is Unchecked., The graph should move to the center of the bounding box (note that it is not the same thing as the viewport
: '''Conclusions:'''
:* GRNsight appears to successfully shift to the center of the bounding box when the Excel workbook is loaded and restrict graph to viewport is unchecked.
; Test 17
: Instructions:
:* Load Graph - File Menu -> Import SIF
:* D-Pad Control - Click Center Button
: Results:
:* GRNsight should lay out a network graph from the SIF file if there are no errors in the file
:* if "Restrict Graph to Viewport" is Unchecked., The graph should move to the center of the bounding box (note that it is not the same thing as the viewport
: '''Conclusions:'''
:* GRNsight appears to successfully shift to the center of the bounding box when the SIF file is loaded and restrict graph to viewport is unchecked.
; Test 18
: Instructions:
:* Load Graph - File Menu -> Import GraphML
:* D-Pad Control - Click Center Button
: Results:
:* GRNsight should lay out a network graph from the GraphML file if there are no errors in the file
:* if "Restrict Graph to Viewport" is Unchecked., The graph should move to the center of the bounding box (note that it is not the same thing as the viewport
: '''Conclusions:'''
:* GRNsight appears to successfully shift to the center of the bounding box when the GraphML file is loaded and restrict graph to viewport is unchecked.

==Part 2: Web Service Exploration==
===== UniProt =====
* Relevant documentation:
** http://www.uniprot.org/help/api
** http://www.uniprot.org/help/api_idmapping
** http://www.uniprot.org/help/api_retrieve_entries
* Supplementary websites:
** [http://www.uniprot.org/uploadlists/ Retrieve/ID mapping] web page
* Technical information:
** You will encounter ''redirects'' in these web services; web browsers handle this automatically, but if using '''curl''' make sure to add the '''-L''' switch (i.e., '''curl -L '''…)
** Your URLs will include ampersands ('''&'''), which will need special handling with '''curl''': in these cases, enclose the URL in apostrophes (e.g., '''curl -L '<nowiki>http://www.uniport.org?query=this&type=that</nowiki>'''')
** UniProt primarily provides results in XML format; in one relevant step, the data can be provided in tab-delimited format, which might be easier to work with
* Miscellaneous information:
** You will encounter the need for a ''taxon ID'', which identifies a specific organism; the taxon ID for our strain of ''S. cerevisiae'' is '''559292'''

===== Solution =====

;This is a bash function that we made to display the results from today's exercise:
# '''NOTE:''' We are assuming that you are using bash for the purposes of running this command.
# put this code into a file named getInfoForGene
# run chmod +x ./getInfoForGene
# use the function like so: ./getInfoForGene "GENE_NAME"
#* ex: ./getInfoForGene PBS2

<pre>#!/bin/bash

gene_id=$(curl -L "http://www.uniprot.org/uploadlists/?from=GENENAME&to=ACC&format=tab&taxon=559292&query=$1" | grep $1 | sed "s/$1[[:space:]]*$[A-Za-z0-9]*$.*/\1/g")

curl -L "http://uniprot.org/uniprot/$gene_id.xml"</pre>

===== Notebook =====

When doing this assignment, we first made a point of identifying and outlining the endpoints that we'd need to go from the gene name to the gene description; of which there were 2: turning the gene name into the matching uniprot id; then turning the uniprot id into the gene's information page. It was pretty clear how to do the latter part, which had been provided to us in an earlier assignment, but to do the former, we needed to use Uniprot's format conversion endpoint. First we found what parameters we'd need to provide to the endpoint, inspecting the elements to find the names of the specific parameters, and we promptly became stuck on the taxon id because our answers weren't being restricted solely to the genes that we wanted. Luckily, however, Dondi's notes provided us with the taxon id we needed, speeding up our process of getting to the answer. After we were able to get the uploadlists endpoint to work, we just needed to extract the id from the tab delimited output. we ran into a problem regarding POSIX-based whitespace, but we resolved it using the sed command you see provided. Finally, we assigned that output to a variable and fed it into the gene description endpoint.

==Links and References==

===Acknowledgements===

#Met outside of class with [[User:Cwong34|Corinne Wong]] to discuss any questions we had prior to meeting and throughout the process of completing the Week 9 assignment.

'''While I worked with the people noted above, this individual journal entry was completed by me and not copied from another source.'''

[[User:Cazinge|Cazinge]] ([[User talk:Cazinge|talk]]) 15:44, 6 November 2017 (PST)

===References===

#LMU BioDB 2017. (2017). Week 9. Retrieved October 17, 2017, from https://xmlpipedb.cs.lmu.edu/biodb/fall2017/index.php/Week_9

{{Template:Cazinge}}

Cazinge Week 10

2017-11-06T23:43:37Z

Cazinge: Added acknowledgements and references and template

==== 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
#Log2 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
# Identifying regulatory transcription factors responsible for observed changes in gene expression
# Dynamical systems modeling of the gene regulatory network ([http://kdahlquist.github.io/GRNmap/ GRNmap])
# Viewing modeling results in [http://dondi.github.io/GRNsight/ GRNsight]

==== Clustering and GO Term Enrichment with stem ====

# '''Prepare your microarray data file for loading into STEM.'''
#* Download your Excel workbook that you used for your [[Week 8]] assignment.
#* Insert a new worksheet into your Excel workbook, and name it "wt_stem".
#* Select all of the data from your "wt_ANOVA" worksheet and Paste special > paste values into your "wt_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. '''''Record the number of genes left in your electronic notebook.'''''
#**** '''ANSWER:''' 2006 genes.
#** 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.andrew.cmu.edu/user/zivbj/stemreg.html download link], register, 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>. 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, select ''Saccharomyces cerevisiae (SGD)'', from the drop-down menu for Gene Annotation Source. Select ''No cross references'', from the Cross Reference Source drop-down menu. Select ''No Gene Locations'' from the Gene Location Source drop-down menu.
## 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.
# '''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 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).
# '''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?'''''
##** '''ANSWER:''' I chose profile 22 because the graph looked interesting, as for the majority of its included genes went from a neutral state to experiencing a positive expression change from the cold shock.
##* '''''How many genes belong to this profile?'''''
##** '''ANSWER:''' 284.0
##* '''''How many genes were expected to belong to this profile?'''''
##** '''ANSWER:''' 29.0
##* '''''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.
##** '''ANSWER:''' 5.9E-181 (significant)
##* 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?'''''
##** '''ANSWER:''' 263 (35.25%) GO terms are associated with profile 22 at p < 0.05. 14 (1.88%) GO terms are associated with profile 22 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 presentation next week. 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 final 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 Δgln3 and Δswi4 groups)?'''''
##*** '''ANSWER:'''
##**** '''cytoplasm:''' Genes associated with cytoplasm are expressed when cold shock occurs as the cell undergoes a period of stress, due to the cytoplasm's need for more energy.
##**** '''carbohydrate derivative metabolic process:''' During cold shock, the cell attempts to produce carbohydrates for oxidation, thus expressing genes of this category.
##**** '''nucleotide metabolic process:''' When the cell experiences a period of stress due to cold shock, it attempts to generate energy via nucleotide metabolic processes, causing it to express genes associated with this category.
##**** '''perioxidase activity:''' During cold shock, the cell attempts oxidation, causing genes associated with perioxidase activity to become expressed.
##**** '''cellular response to toxic substance:''' A cell in cold shock exhibits similar characteristics as cells responding to toxic substances, causing genes of the latter category to be expressed.
##**** '''organophosphate metabolic process:''' During cold shock, the cell attempts to produce phosphate for oxidation, thus expressing genes of this category.
##** 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 at center top of the page called "Search GO Data".
##** 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].

'''''This is the stopping point for the Week 10 Assignment.''''' We will pick up the next steps in the analysis in subsequent weeks.

==== Using YEASTRACT to Infer which Transcription Factors Regulate a Cluster of Genes ====

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.

# 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.
# Launch a web browser and go to the [http://www.yeastract.com/ YEASTRACT database].
#* On the left panel of the window, click on the link to [http://www.yeastract.com/formrankbytf.php ''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.
# 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"?'''''
#* '''''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 OWW or Box and link to it in your electronic lab notebook.'''''
#** '''''Is your transcription factor on the list? If so, what is their "% in user set", "% in YEASTRACT", and "p value".''''' (Note that this doesn't apply to the wt strain).
# For the mathematical model and GRNsight, 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 15-30 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 and HAP4 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.
#* Go back to the YEASTRACT database and follow the link to ''[http://www.yeastract.com/formgenerateregulationmatrix.php Generate Regulation Matrix]''.
#* Copy and paste the list of transcription factors you identified (plus the transcription factor deleted in your strain) 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.
# First we need to properly format the output files from YEASTRACT. You will repeat these steps for each of the three files you generated above.
#* 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).
#* Check to see that all of the transcription factors in the matrix are connected to at least one of the other transcription factors by making sure that there is at least one "1" in a row or column for that transcription factor. If a factor is not connected to any other factor, delete its row and column from the matrix. Make sure that you still have somewhere between 15 and 30 transcription factors in your network after this pruning.
#** Only delete the transcription factor if there are all zeros in its column '''AND''' all zeros in its row. You may find visualizing the matrix in GRNsight (below) can help you find these easily.
#* 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.
# Now we will visualize what these gene regulatory networks look like with the GRNsight software.
#* Go to the [http://dondi.github.io/GRNsight/ 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. Move 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.

==== Conclusion ====

In this experiment, we (almost) finished analyzing the DNA microarray dataset from Dahlquist's wetlab via a STEM analysis of the data. After pruning our data set solely to the p values and AvgLogFC's per timepoint for each gene, we ran our data through the STEM software provided to us. This produced a number of profiles comprised of genes expression curves and associated genes which had similar shapes to one another. After formatting and uploading this data to the wiki, I filtered the data in profile 22, taking only those that had corrected p values only .05, and found explanations as to why the genes in 6 of the profile's categories became expressed during cold shock. Due to time constraints, this is where the Week 10 assignment ends.

==Acknowledgements==

#Met outside of class with [[User:Emmatyrnauer|Emma Tyrnauer]] to discuss any questions we had prior to meeting and throughout the process of completing the Week 10 assignment.

'''While I worked with the people noted above, this individual journal entry was completed by me and not copied from another source.'''

[[User:Cazinge|Cazinge]] ([[User talk:Cazinge|talk]]) 15:43, 6 November 2017 (PST)
==References==

#LMU BioDB 2017. (2017). Week 10. Retrieved November 3, 2017, from https://xmlpipedb.cs.lmu.edu/biodb/fall2017/index.php/Week_10
#Gene Ontology Consortium. (2017). Retrieved November 3, 2017, from http://geneontology.org

{{Template:Cazinge}}

Cazinge Week 8

2017-11-06T23:43:35Z

Cazinge: adding signiture

==Electronic Notebook==
=== Microarray Data Analysis ===

We will be working on the protocols in class on Tuesday, October 17 and Thursday, October 19. Whatever you do not finish in class will be homework to be completed by the Week 8 journal deadline.

==== 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
#Log2 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
# Identifying regulatory transcription factors responsible for observed changes in gene expression
# Dynamical systems modeling of the gene regulatory network ([http://kdahlquist.github.io/GRNmap/ GRNmap])
# Viewing modeling results in [http://dondi.github.io/GRNsight/ GRNsight]

You will download the starting Excel spreadsheet from [https://lmu.app.box.com/login Box]. You were e-mailed a link to do this before class.

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

* In the Excel spreadsheet, there is a worksheet labeled "Master_Sheet".
** 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 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 log2 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, "dASH1" for the ''Δash1'' data, etc.). "LogFC" stands for "Log2 Fold Change" which is the Log2 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.'''''
** '''ANSWER:''' wild type data (wt); BIOL367_Fall2017_Dahlquist-microarray-data-master_20171017_EA.xlsx; (Timepoint=#replicates) wt: t15=4, t30=5, t60=4, t90=5, t120=5
* '''NOTE: before beginning any analysis, immediately change the filename so that it contains your initials to distinguish it from other students' work.'''
** '''ANSWER:''' Changed to include my initials.
* The first thing you will do is to delete the data columns of all strains other than those prefixed with wt.
* Next you will replace cells that have "NA" in them (which indicates missing data) with an empty cell.
** Use the keyboard shortcut Control+F to open the "Find" dialog box and select the "Replace" tab.
** Type "NA" in the Search field and don't type anything in the "Replace" field.
** Click the button "Replace all" and '''record the number of replacements made in your electronic lab notebook.'''
*** '''ANSWER:''' 4071 instances replaced
** Save early and often throughout this protocol. We are working with a large spreadsheet and glitches do occur.

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

The purpose of the witin-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 "wt_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 wt_AvgLogFC_(TIME) where wt is your strain designation and (TIME) is 15, 30, etc.
# In the cell below the wt_AvgLogFC_t15 header, type <code>=AVERAGE(</code>
# Then highlight all the data in row 2 associated with wt and 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 wt_AvgLogFC_t120 calculation, create the column header wt_ss_HO.
# In the first cell below this header, type <code>=SUMSQ(</code>
# Highlight all the LogFC data in row 2 for your wt (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 wt_ss_HO, create the column headers wt_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 wt_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 wt_ss_t120, create the column header wt_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 wt_Fstat and wt_p-value.
# Recall the number of data points from (13): call that total n.
# In the first cell of the wt_Fstat column, type <code>=((n-5)/5)*(<wt_ss_HO>-<wt_SS_full>)/<wt_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 and the dSWI4 strain has 4 timepoints (it is missing t15).
#* Replace the phrase wt_ss_HO with the cell designation.
#* Replace the phrase <wt_SS_full> with the cell designation.
#* Copy to the whole column.
# In the first cell below the wt_p-value header, type <code>=FDIST(<wt_Fstat>,5,n-5)</code> replacing the phrase <wt_Fstat> with the cell designation and the "n" as in (13) with the number of data points total. (Again, note that the number of timepoints is actually "4" for the dSWI4 strain). 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 wt_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.

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

# 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, wt_Bonferroni_p-value.
# Type the equation <code>=<wt_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 wt_Bonferroni_p-value header: <code>=IF(wt_Bonferroni_p-value>1,1,wt_Bonferroni_p-value)</code>, where "wt_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 "wt_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 wt_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 "wt_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.'''''
** '''ANSWER:''' [[Media:BIOL367_Fall2017_Dahlquist-microarray-data-master_20171017_EA.zip]]

==== 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 wt_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)?'''''
*** '''ANSWER:''' 2528, 40.8466%
** '''''How many genes have p < 0.01? and what is the percentage (out of 6189)?'''''
*** '''ANSWER:''' 1652, 26.6925%
** '''''How many genes have p < 0.001? and what is the percentage (out of 6189)?'''''
*** '''ANSWER:''' 919, 14.8489%
** '''''How many genes have p < 0.0001? and what is the percentage (out of 6189)?'''''
*** '''ANSWER:''' 496, 8.0142%
* 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)?'''''
*** '''ANSWER:''' 248, 4.0071%
** '''''How many genes are p < 0.05 for the Benjamini and Hochberg-corrected p value? and what is the percentage (out of 6189)?'''''
*** '''ANSWER:''' 2006, 32.4123%
* 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_F17_sample_p-value_slide.pptx | sample PowerPoint slide]] to see how your table should be formatted. Upload your slide to the wiki.
** '''ANSWER:''' [[Media:Wt_p-value_slide_EA.pptx]]
** 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?
** '''ANSWER:''' Unadjusted: 2.8690e-10; Bonferroni-corrected: 1.77563e-6; B-H-corrected: 7.52066e-10; t15=3.27923, t30=3.621, t60=3.5265, t90=-2.0498, t120=-0.6062; Yes, ''NSR1'' changes expression due to cold shock between times t60 and t90. It becomes expressed as it's Log2FC ratio changes from positive to negative.
* For fun, find "your favorite gene" (from your web page) 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?
** '''ANSWER:''' Unadjusted: .3616; Bonferroni-corrected: 1; B-H-corrected: 0.4868; t15=0.4699, t30=-0.4585, t60=-0.3564, t90=0.9098, t120=-0.1191; Yes, ''NSR1'' changes expression due to cold shock between times t60 and t90. It becomes slightly repressed as it's Log2FC ratio changes from negative to positive.

==== Summary Paragraph ====

In this experiment, we analyzed a DNA microarray dataset from Dahlquist's wetlab by preforming a number of transformations to the data. After averaging the data and finding its sum of squares, we were able to extrabolate an F-stat and derive a p-value for each of the genes of the wild type strain's data. After this process, we recieved p-values for which 40% of the dataset had p < .05. In order to correct this, we preformed a Bonferrani p-value correction, and a Benjamin & Hochberg p-value correction, the first of which returned 4% of our genes as having p-values under .05, and the latter, which returned 32% of our dataset as having p-values underneath .05. Finally, we analyzed NSR1 and CLN1, which were expressed and repressed by cold shock respectively.

==Acknowledgements==

#Met outside of class with [[User:Emmatyrnauer|Emma Tyrnauer]] to discuss any questions we had prior to meeting and throughout the process of completing the Week 8 assignment.

'''While I worked with the people noted above, this individual journal entry was completed by me and not copied from another source.'''

[[User:Cazinge|Cazinge]] ([[User talk:Cazinge|talk]]) 15:43, 6 November 2017 (PST)

==References==

#LMU BioDB 2017. (2017). Week 8. Retrieved October 10, 2017, from https://xmlpipedb.cs.lmu.edu/biodb/fall2017/index.php/Week_8

{{Template:Cazinge}}

Cazinge Week 10

2017-11-06T23:40:57Z

Cazinge: /* Summary of what you need to turn in for the individual Week 10 assignment */

Cazinge Week 10

2017-11-06T23:25:15Z

Cazinge: updating to my gene

==== 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
#Log2 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
# Identifying regulatory transcription factors responsible for observed changes in gene expression
# Dynamical systems modeling of the gene regulatory network ([http://kdahlquist.github.io/GRNmap/ GRNmap])
# Viewing modeling results in [http://dondi.github.io/GRNsight/ GRNsight]

==== Clustering and GO Term Enrichment with stem ====

# '''Prepare your microarray data file for loading into STEM.'''
#* Download your Excel workbook that you used for your [[Week 8]] assignment.
#* Insert a new worksheet into your Excel workbook, and name it "wt_stem".
#* Select all of the data from your "wt_ANOVA" worksheet and Paste special > paste values into your "wt_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. '''''Record the number of genes left in your electronic notebook.'''''
#**** '''ANSWER:''' 2006 genes.
#** 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.andrew.cmu.edu/user/zivbj/stemreg.html download link], register, 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>. 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, select ''Saccharomyces cerevisiae (SGD)'', from the drop-down menu for Gene Annotation Source. Select ''No cross references'', from the Cross Reference Source drop-down menu. Select ''No Gene Locations'' from the Gene Location Source drop-down menu.
## 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.
# '''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 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).
# '''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?'''''
##** '''ANSWER:''' I chose profile 22 because the graph looked interesting, as for the majority of its included genes went from a neutral state to experiencing a positive expression change from the cold shock.
##* '''''How many genes belong to this profile?'''''
##** '''ANSWER:''' 284.0
##* '''''How many genes were expected to belong to this profile?'''''
##** '''ANSWER:''' 29.0
##* '''''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.
##** '''ANSWER:''' 5.9E-181 (significant)
##* 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?'''''
##** '''ANSWER:''' 263 (35.25%) GO terms are associated with profile 22 at p < 0.05. 14 (1.88%) GO terms are associated with profile 22 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 presentation next week. 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 final 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 Δgln3 and Δswi4 groups)?'''''
##*** '''ANSWER:'''
##**** '''cytoplasm:''' Genes associated with cytoplasm are expressed when cold shock occurs as the cell undergoes a period of stress, due to the cytoplasm's need for more energy.
##**** '''carbohydrate derivative metabolic process:''' During cold shock, the cell attempts to produce carbohydrates for oxidation, thus expressing genes of this category.
##**** '''nucleotide metabolic process:''' When the cell experiences a period of stress due to cold shock, it attempts to generate energy via nucleotide metabolic processes, causing it to express genes associated with this category.
##**** '''perioxidase activity:''' During cold shock, the cell attempts oxidation, causing genes associated with perioxidase activity to become expressed.
##**** '''cellular response to toxic substance:''' A cell in cold shock exhibits similar characteristics as cells responding to toxic substances, causing genes of the latter category to be expressed.
##**** '''organophosphate metabolic process:''' During cold shock, the cell attempts to produce phosphate for oxidation, thus expressing genes of this category.
##** 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 at center top of the page called "Search GO Data".
##** 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].

'''''This is the stopping point for the Week 10 Assignment.''''' We will pick up the next steps in the analysis in subsequent weeks.

==== Using YEASTRACT to Infer which Transcription Factors Regulate a Cluster of Genes ====

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.

# 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.
# Launch a web browser and go to the [http://www.yeastract.com/ YEASTRACT database].
#* On the left panel of the window, click on the link to [http://www.yeastract.com/formrankbytf.php ''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.
# 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"?'''''
#* '''''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 OWW or Box and link to it in your electronic lab notebook.'''''
#** '''''Is your transcription factor on the list? If so, what is their "% in user set", "% in YEASTRACT", and "p value".''''' (Note that this doesn't apply to the wt strain).
# For the mathematical model and GRNsight, 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 15-30 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 and HAP4 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.
#* Go back to the YEASTRACT database and follow the link to ''[http://www.yeastract.com/formgenerateregulationmatrix.php Generate Regulation Matrix]''.
#* Copy and paste the list of transcription factors you identified (plus the transcription factor deleted in your strain) 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.
# First we need to properly format the output files from YEASTRACT. You will repeat these steps for each of the three files you generated above.
#* 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).
#* Check to see that all of the transcription factors in the matrix are connected to at least one of the other transcription factors by making sure that there is at least one "1" in a row or column for that transcription factor. If a factor is not connected to any other factor, delete its row and column from the matrix. Make sure that you still have somewhere between 15 and 30 transcription factors in your network after this pruning.
#** Only delete the transcription factor if there are all zeros in its column '''AND''' all zeros in its row. You may find visualizing the matrix in GRNsight (below) can help you find these easily.
#* 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.
# Now we will visualize what these gene regulatory networks look like with the GRNsight software.
#* Go to the [http://dondi.github.io/GRNsight/ 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. Move 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.

==== Summary of what you need to turn in for the individual Week 10 assignment ====

# Your individual journal page should have an '''''electronic lab notebook''''' recording your work. This includes the detailed methods specific to your analysis, your result files, the answers to any questions posed in the protocol above, a scientific conclusion, and the acknowledgments and references sections. Don't forget your paragraph which is a biological interpretation of your stem results.
# Upload your updated Excel spreadsheet to the wiki that has today's manipulations in it. Use the same filename as before so that the download link that you already (previous versions will still be available in the history).
# Append the screenshots of the stem results to the PowerPoint presentation that contains the p value table that you created for the [[Week 8]] assignments. Each slide in the presentation should have a meaningful title that describes the main message of the slide. Include the screenshot of the GRNsight-visualized network in the PowerPoint.
# Zip together all of the tab-delimited text files that you created for and from stem and upload them to the wiki.
#* the file that was saved from your original spreadsheet that you used to run stem
#* each of the genelist and GOlist files for each of your significant profiles.
# Write a paragraph-length conclusion for this week's exercise.

Cazinge Week 10

2017-11-06T23:09:11Z

Cazinge: /* Clustering and GO Term Enrichment with stem */ formatting

==== 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
#Log2 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
# Identifying regulatory transcription factors responsible for observed changes in gene expression
# Dynamical systems modeling of the gene regulatory network ([http://kdahlquist.github.io/GRNmap/ GRNmap])
# Viewing modeling results in [http://dondi.github.io/GRNsight/ GRNsight]

==== Clustering and GO Term Enrichment with stem ====

# '''Prepare your microarray data file for loading into STEM.'''
#* Download your Excel workbook that you used for your [[Week 8]] assignment.
#* 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. '''''Record the number of genes left in your electronic notebook.'''''
#**** '''ANSWER:''' 2006 genes.
#** 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.andrew.cmu.edu/user/zivbj/stemreg.html download link], register, 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>. 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, select ''Saccharomyces cerevisiae (SGD)'', from the drop-down menu for Gene Annotation Source. Select ''No cross references'', from the Cross Reference Source drop-down menu. Select ''No Gene Locations'' from the Gene Location Source drop-down menu.
## 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.
# '''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 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).
# '''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?'''''
##** '''ANSWER:''' I chose profile 22 because the graph looked interesting, as for the majority of its included genes went from a neutral state to experiencing a positive expression change from the cold shock.
##* '''''How many genes belong to this profile?'''''
##** '''ANSWER:''' 284.0
##* '''''How many genes were expected to belong to this profile?'''''
##** '''ANSWER:''' 29.0
##* '''''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.
##** '''ANSWER:''' 5.9E-181 (significant)
##* 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?'''''
##** '''ANSWER:''' 263 (35.25%) GO terms are associated with profile 22 at p < 0.05. 14 (1.88%) GO terms are associated with profile 22 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 presentation next week. 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 final 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 Δgln3 and Δswi4 groups)?'''''
##*** '''ANSWER:'''
##**** '''cytoplasm:''' Genes associated with cytoplasm are expressed when cold shock occurs as the cell undergoes a period of stress, due to the cytoplasm's need for more energy.
##**** '''carbohydrate derivative metabolic process:''' During cold shock, the cell attempts to produce carbohydrates for oxidation, thus expressing genes of this category.
##**** '''nucleotide metabolic process:''' When the cell experiences a period of stress due to cold shock, it attempts to generate energy via nucleotide metabolic processes, causing it to express genes associated with this category.
##**** '''perioxidase activity:''' During cold shock, the cell attempts oxidation, causing genes associated with perioxidase activity to become expressed.
##**** '''cellular response to toxic substance:''' A cell in cold shock exhibits similar characteristics as cells responding to toxic substances, causing genes of the latter category to be expressed.
##**** '''organophosphate metabolic process:''' During cold shock, the cell attempts to produce phosphate for oxidation, thus expressing genes of this category.
##** 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 at center top of the page called "Search GO Data".
##** 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].

'''''This is the stopping point for the Week 10 Assignment.''''' We will pick up the next steps in the analysis in subsequent weeks.

==== Using YEASTRACT to Infer which Transcription Factors Regulate a Cluster of Genes ====

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.

# 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.
# Launch a web browser and go to the [http://www.yeastract.com/ YEASTRACT database].
#* On the left panel of the window, click on the link to [http://www.yeastract.com/formrankbytf.php ''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.
# 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"?'''''
#* '''''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 OWW or Box and link to it in your electronic lab notebook.'''''
#** '''''Is your transcription factor on the list? If so, what is their "% in user set", "% in YEASTRACT", and "p value".''''' (Note that this doesn't apply to the wt strain).
# For the mathematical model and GRNsight, 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 15-30 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 and HAP4 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.
#* Go back to the YEASTRACT database and follow the link to ''[http://www.yeastract.com/formgenerateregulationmatrix.php Generate Regulation Matrix]''.
#* Copy and paste the list of transcription factors you identified (plus the transcription factor deleted in your strain) 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.
# First we need to properly format the output files from YEASTRACT. You will repeat these steps for each of the three files you generated above.
#* 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).
#* Check to see that all of the transcription factors in the matrix are connected to at least one of the other transcription factors by making sure that there is at least one "1" in a row or column for that transcription factor. If a factor is not connected to any other factor, delete its row and column from the matrix. Make sure that you still have somewhere between 15 and 30 transcription factors in your network after this pruning.
#** Only delete the transcription factor if there are all zeros in its column '''AND''' all zeros in its row. You may find visualizing the matrix in GRNsight (below) can help you find these easily.
#* 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.
# Now we will visualize what these gene regulatory networks look like with the GRNsight software.
#* Go to the [http://dondi.github.io/GRNsight/ 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. Move 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.

==== Summary of what you need to turn in for the individual Week 10 assignment ====

# Your individual journal page should have an '''''electronic lab notebook''''' recording your work. This includes the detailed methods specific to your analysis, your result files, the answers to any questions posed in the protocol above, a scientific conclusion, and the acknowledgments and references sections. Don't forget your paragraph which is a biological interpretation of your stem results.
# Upload your updated Excel spreadsheet to the wiki that has today's manipulations in it. Use the same filename as before so that the download link that you already (previous versions will still be available in the history).
# Append the screenshots of the stem results to the PowerPoint presentation that contains the p value table that you created for the [[Week 8]] assignments. Each slide in the presentation should have a meaningful title that describes the main message of the slide. Include the screenshot of the GRNsight-visualized network in the PowerPoint.
# Zip together all of the tab-delimited text files that you created for and from stem and upload them to the wiki.
#* the file that was saved from your original spreadsheet that you used to run stem
#* each of the genelist and GOlist files for each of your significant profiles.
# Write a paragraph-length conclusion for this week's exercise.

Cazinge Week 10

2017-11-06T23:08:01Z

Cazinge: /* Clustering and GO Term Enrichment with stem */ adding reasons for cold shock

==== 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
#Log2 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
# Identifying regulatory transcription factors responsible for observed changes in gene expression
# Dynamical systems modeling of the gene regulatory network ([http://kdahlquist.github.io/GRNmap/ GRNmap])
# Viewing modeling results in [http://dondi.github.io/GRNsight/ GRNsight]

==== Clustering and GO Term Enrichment with stem ====

# '''Prepare your microarray data file for loading into STEM.'''
#* Download your Excel workbook that you used for your [[Week 8]] assignment.
#* 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. '''''Record the number of genes left in your electronic notebook.'''''
#**** '''ANSWER:''' 2006 genes.
#** 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.andrew.cmu.edu/user/zivbj/stemreg.html download link], register, 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>. 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, select ''Saccharomyces cerevisiae (SGD)'', from the drop-down menu for Gene Annotation Source. Select ''No cross references'', from the Cross Reference Source drop-down menu. Select ''No Gene Locations'' from the Gene Location Source drop-down menu.
## 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.
# '''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 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).
# '''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?'''''
##** '''ANSWER:''' I chose profile 22 because the graph looked interesting, as for the majority of its included genes went from a neutral state to experiencing a positive expression change from the cold shock.
##* '''''How many genes belong to this profile?'''''
##** '''ANSWER:''' 284.0
##* '''''How many genes were expected to belong to this profile?'''''
##** '''ANSWER:''' 29.0
##* '''''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.
##** '''ANSWER:''' 5.9E-181 (significant)
##* 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?'''''
##** '''ANSWER:''' 263 (35.25%) GO terms are associated with profile 22 at p < 0.05. 14 (1.88%) GO terms are associated with profile 22 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 presentation next week. 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 final 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 Δgln3 and Δswi4 groups)?'''''
##*** '''ANSWER:'''
##**** cytoplasm: Genes associated with cytoplasm are expressed when cold shock occurs as the cell undergoes a period of stress, due to the cytoplasm's need for more energy.
##**** carbohydrate derivative metabolic process: During cold shock, the cell attempts to produce carbohydrates for oxidation, thus expressing genes of this category.
##**** nucleotide metabolic process: When the cell experiences a period of stress due to cold shock, it attempts to generate energy via nucleotide metabolic processes, causing it to express genes associated with this category.
##**** perioxidase activity: During cold shock, the cell attempts oxidation, causing genes associated with perioxidase activity to become expressed.
##**** cellular response to toxic substance: A cell in cold shock exhibits similar characteristics as cells responding to toxic substances, causing genes of the latter category to be expressed.
##**** organophosphate metabolic process: During cold shock, the cell attempts to produce phosphate for oxidation, thus expressing genes of this category.
##** 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 at center top of the page called "Search GO Data".
##** 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].

'''''This is the stopping point for the Week 10 Assignment.''''' We will pick up the next steps in the analysis in subsequent weeks.

==== Using YEASTRACT to Infer which Transcription Factors Regulate a Cluster of Genes ====

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.

# 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.
# Launch a web browser and go to the [http://www.yeastract.com/ YEASTRACT database].
#* On the left panel of the window, click on the link to [http://www.yeastract.com/formrankbytf.php ''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.
# 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"?'''''
#* '''''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 OWW or Box and link to it in your electronic lab notebook.'''''
#** '''''Is your transcription factor on the list? If so, what is their "% in user set", "% in YEASTRACT", and "p value".''''' (Note that this doesn't apply to the wt strain).
# For the mathematical model and GRNsight, 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 15-30 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 and HAP4 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.
#* Go back to the YEASTRACT database and follow the link to ''[http://www.yeastract.com/formgenerateregulationmatrix.php Generate Regulation Matrix]''.
#* Copy and paste the list of transcription factors you identified (plus the transcription factor deleted in your strain) 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.
# First we need to properly format the output files from YEASTRACT. You will repeat these steps for each of the three files you generated above.
#* 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).
#* Check to see that all of the transcription factors in the matrix are connected to at least one of the other transcription factors by making sure that there is at least one "1" in a row or column for that transcription factor. If a factor is not connected to any other factor, delete its row and column from the matrix. Make sure that you still have somewhere between 15 and 30 transcription factors in your network after this pruning.
#** Only delete the transcription factor if there are all zeros in its column '''AND''' all zeros in its row. You may find visualizing the matrix in GRNsight (below) can help you find these easily.
#* 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.
# Now we will visualize what these gene regulatory networks look like with the GRNsight software.
#* Go to the [http://dondi.github.io/GRNsight/ 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. Move 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.

==== Summary of what you need to turn in for the individual Week 10 assignment ====

# Your individual journal page should have an '''''electronic lab notebook''''' recording your work. This includes the detailed methods specific to your analysis, your result files, the answers to any questions posed in the protocol above, a scientific conclusion, and the acknowledgments and references sections. Don't forget your paragraph which is a biological interpretation of your stem results.
# Upload your updated Excel spreadsheet to the wiki that has today's manipulations in it. Use the same filename as before so that the download link that you already (previous versions will still be available in the history).
# Append the screenshots of the stem results to the PowerPoint presentation that contains the p value table that you created for the [[Week 8]] assignments. Each slide in the presentation should have a meaningful title that describes the main message of the slide. Include the screenshot of the GRNsight-visualized network in the PowerPoint.
# Zip together all of the tab-delimited text files that you created for and from stem and upload them to the wiki.
#* the file that was saved from your original spreadsheet that you used to run stem
#* each of the genelist and GOlist files for each of your significant profiles.
# Write a paragraph-length conclusion for this week's exercise.

Cazinge Week 10

2017-11-06T22:45:43Z

Cazinge: /* Clustering and GO Term Enrichment with stem */ filling out template

==== 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
#Log2 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
# Identifying regulatory transcription factors responsible for observed changes in gene expression
# Dynamical systems modeling of the gene regulatory network ([http://kdahlquist.github.io/GRNmap/ GRNmap])
# Viewing modeling results in [http://dondi.github.io/GRNsight/ GRNsight]

==== Clustering and GO Term Enrichment with stem ====

# '''Prepare your microarray data file for loading into STEM.'''
#* Download your Excel workbook that you used for your [[Week 8]] assignment.
#* 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. '''''Record the number of genes left in your electronic notebook.'''''
#**** '''ANSWER:''' 2006 genes.
#** 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.andrew.cmu.edu/user/zivbj/stemreg.html download link], register, 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>. 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, select ''Saccharomyces cerevisiae (SGD)'', from the drop-down menu for Gene Annotation Source. Select ''No cross references'', from the Cross Reference Source drop-down menu. Select ''No Gene Locations'' from the Gene Location Source drop-down menu.
## 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.
# '''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 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).
# '''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?'''''
##** '''ANSWER:''' I chose profile 22 because the graph looked interesting, as for the majority of its included genes went from a neutral state to experiencing a positive expression change from the cold shock.
##* '''''How many genes belong to this profile?'''''
##** '''ANSWER:''' 284.0
##* '''''How many genes were expected to belong to this profile?'''''
##** '''ANSWER:''' 29.0
##* '''''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.
##** '''ANSWER:''' 5.9E-181 (significant)
##* 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?'''''
##** '''ANSWER:''' 263 (35.25%) GO terms are associated with profile 22 at p < 0.05. 14 (1.88%) GO terms are associated with profile 22 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 presentation next week. 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 final 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 Δgln3 and Δswi4 groups)?'''''
##*** '''ANSWER:'''
##**** cytoplasm:
##**** nucleotide metabolic process:
##**** perioxidase activity:
##**** cellular response to toxic substance:
##**** organophosphate metabolic process:
##**** carbohydrate-derivative metabolic process:
##** 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 at center top of the page called "Search GO Data".
##** 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].

'''''This is the stopping point for the Week 10 Assignment.''''' We will pick up the next steps in the analysis in subsequent weeks.

==== Using YEASTRACT to Infer which Transcription Factors Regulate a Cluster of Genes ====

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.

# 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.
# Launch a web browser and go to the [http://www.yeastract.com/ YEASTRACT database].
#* On the left panel of the window, click on the link to [http://www.yeastract.com/formrankbytf.php ''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.
# 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"?'''''
#* '''''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 OWW or Box and link to it in your electronic lab notebook.'''''
#** '''''Is your transcription factor on the list? If so, what is their "% in user set", "% in YEASTRACT", and "p value".''''' (Note that this doesn't apply to the wt strain).
# For the mathematical model and GRNsight, 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 15-30 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 and HAP4 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.
#* Go back to the YEASTRACT database and follow the link to ''[http://www.yeastract.com/formgenerateregulationmatrix.php Generate Regulation Matrix]''.
#* Copy and paste the list of transcription factors you identified (plus the transcription factor deleted in your strain) 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.
# First we need to properly format the output files from YEASTRACT. You will repeat these steps for each of the three files you generated above.
#* 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).
#* Check to see that all of the transcription factors in the matrix are connected to at least one of the other transcription factors by making sure that there is at least one "1" in a row or column for that transcription factor. If a factor is not connected to any other factor, delete its row and column from the matrix. Make sure that you still have somewhere between 15 and 30 transcription factors in your network after this pruning.
#** Only delete the transcription factor if there are all zeros in its column '''AND''' all zeros in its row. You may find visualizing the matrix in GRNsight (below) can help you find these easily.
#* 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.
# Now we will visualize what these gene regulatory networks look like with the GRNsight software.
#* Go to the [http://dondi.github.io/GRNsight/ 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. Move 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.

==== Summary of what you need to turn in for the individual Week 10 assignment ====

# Your individual journal page should have an '''''electronic lab notebook''''' recording your work. This includes the detailed methods specific to your analysis, your result files, the answers to any questions posed in the protocol above, a scientific conclusion, and the acknowledgments and references sections. Don't forget your paragraph which is a biological interpretation of your stem results.
# Upload your updated Excel spreadsheet to the wiki that has today's manipulations in it. Use the same filename as before so that the download link that you already (previous versions will still be available in the history).
# Append the screenshots of the stem results to the PowerPoint presentation that contains the p value table that you created for the [[Week 8]] assignments. Each slide in the presentation should have a meaningful title that describes the main message of the slide. Include the screenshot of the GRNsight-visualized network in the PowerPoint.
# Zip together all of the tab-delimited text files that you created for and from stem and upload them to the wiki.
#* the file that was saved from your original spreadsheet that you used to run stem
#* each of the genelist and GOlist files for each of your significant profiles.
# Write a paragraph-length conclusion for this week's exercise.