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Project Page: GRNsight Gene Page Project

GRNsight

Project Schedule Checklist

[x]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.

Data Analyst

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.

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:
  1. Review the ANOVA results from Week 8 for accuracy, making corrections if necessary.
  2. If corrections were made to the ANOVA results, re-running stem (Week 10).
  3. Using the YEASTRACT Database to determine which transcription factors are candidates for regulating the genes in a cluster from stem (part of Week 10 that we postponed.
  4. Using the YEASTRACT Database to develop a candidate gene regulatory network (part of Week 10 that we postponed).
  5. Using the GRNmap software to model the gene regulatory network (confer with Dr. Dahlquist when you are ready for this).
  6. Visualizing the results with 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

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.

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.
• Page Design team: QA should get to know the information that is expected to be displayed on the gene page.
• 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.
• JASPAR team: QA should learn about transcription factors in general and what the JASPAR database provides in particular. They should coordinate with the Coders to learn the API initially (since this API has not been seen before in class) then work out how to retrieve relevant transcription factor information for a given gene symbol. Once the steps are learned/discovered, 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.
• Interaction and integration team: QA should be familiar with the vision of the project’s overall goal so that when they are presented with progressive builds of the project, they can test the functionality from end to end in order to detect flaws or points of improvement.

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:

• Page Design team: Open the prototype web page and check it for correctness and clarity. The Data Analysis guild should also be consulted to make sure that the page design and layout meets their needs when they perform their respective Data Analysis tasks.
• 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 getGeneInformation, 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).
• For the Interaction and Integration team, prior to the first integration, QA can make sure that the “click-to-open-the-page” functionality works well and is bug-free. After the first integration, QA should work through the whole cycle of opening a gene page and examining the data that appear. QA should open multiple gene pages in the same session to make sure that successive gene page openings do not “step on” each other or leave behind obsolete data that “pollutes” future gene page loads.

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