Kevin Wyllie Week 15

Final Presentation

GENialOMICS Final Presentation

Electronic Lab Notebook

MAPPFinder Procedure

1. MAPPFinder was launched from the GenMAPP window: "Tools" > "MAPPFinder".
2. "Calculate New Results" was selected from the MAPPFinder window.
3. "Find File" was selected and in the directory, the .gex file (created in the Expression Dataset Manager - see week 14) was selected.
   • This file can be found here.
4. Under the "Select Color Set" field, "KWVP_20151205" was selected (the color set's name in this case is the same as the .gex file's name - and there's only one color set in the .gex file).
5. After the color set has been selected, under the "Select Criteria to filter by" field, either increase or decrease was chosen.
6. The boxes for "Gene Ontology" and "Click here to calculate p values..." were checked.
7. The location to save the resulting .txt file was selected with the "Browse" option toward the bottom of the window. Note that because this protocol was carried out both for increase and decrease, there were two text files generated in MAPPFinder.
8. "Run MAPPFinder" was selected to generate the gene ontology tree (and the aforementioned .txt files).
   • The files generated can be found here:
     • For downregulated GO terms.
     • For upregulated GO terms.

Filtered GO List

• The GO term files above were opened in Excel, and the following filters were placed on the given columns:
  • Z score: greater than 2
  • PermuteP: less than 0.05
  • Number changed: greater than or equal to 4 AND less than 100
  • Percent changed: great than or equal to 15
• Values in the following columns were recorded:
  • Number Changed
  • Number Measured
  • Number in GO
  • Percent Changed
  • Percent Present
  • PermuteP
  • AdjustedP
• Some of the above filter criteria had to be adjusted to attain 16 non-redundant GO terms. A list of these terms is shown below. For those terms which did not exactly fit this initial criteria, the altered criteria is apparent in the values on the table.

Sanity Check

Reflection

Statement of Work

The goal of this project was to create a gene database for Burkholderia cenocepacia and to use this database for the purpose of conducting an analysis on microarray data generated by Van Acker et al (2013). This data measured the transcriptomic effects of tobramycin (an antibiotic) on the bacterium. The database was constructed using data from Burkholderia Genome Database, UniProt and Gene Ontology Consortium. The microarray data was taken from the paper, Biofilm-Grown Burkholderia cepacia Complex Cells Survive Antibiotic Treatment by Avoiding Production of Reactive Oxygen Species. Once the gene database (found here) was created, the processed microarray data (found here) were fed through a software called GenMAPP, which analyzed the data and returned significantly changed cellular processes (gene ontology terms - see above for files and results). We then compared the biological implications of our analysis to that of Van Acker et al (2013).

Assessment of Project

This project, I think, was a success. I can't speak for the QA and Coder (though the process seemed to go smoothly for them as well), but Veronica and I faced a healthy difficulty curve (substantial, yet reasonable) in processing and analyzing the microarray data. The only thing that "didn't" work for us was initially proceeding further than we should've with statistical analysis, which ended up being a waste of time as we had not properly accounted for the multitude of technical replicates within our data. But retracing our steps wasn't horribly difficult. To be quite honest, if I were to do this project again, the only thing I would change is that I would stress out about it less, because it was much easier than I had expected!

I believe the quality of our work was high. We got slightly ahead of schedule, and thus had more time afterward to carefully examine our methods and verify that we had processed the data correctly. The organization may have been the most difficult part at first, managing between my individual page and the group page. The requirements for the group page initially seemed overwhelming, but it wasn't a problem in the end. We certainly completed all of the "milestones" in this project. Again, this was not an issue because we overestimated the difficult of this project and thus spent ample time on it.

Reflection on the Process

• What did you learn?
  • With your head?
    • I learned a lot about the microbial response to certain antibiotics! But more broadly, this project gave me a deeper understanding of the importance of deeply comprehending the scientific reasons for carrying out each step in the process of analyzing results.
  • With your heart?
    • I think this project taught me a lot about how to communicate with people of different skillsets. Although every student in the class did the same assignments before this project, upon beginning this project we each re-familiarized ourselves with the appropriate processes to carry out our specific roles (and, being busy college students, only those necessary processes). Thus, when communicating with Anu and Brandon, I had to find ways to explain my part of the project to someone who didn't know as much about the data processing protocols (and that's totally ok; it wasn't their responsibility to know these things, and this is mirrored in real workplace settings). Of course, when they communicated with Veronica and I, they had to water down their protocols as well.
  • With your hands (technical skills)?
    • Every time I do microarray analysis-related tasks in Excel (whether in this class or with the Dahlquist Lab), it feels like I get a little better at it. And this project was no different. Excel, in general, is a skill that develops through repeated use, so that's certainly a technical skill I developed further. However, in this project we faced the added difficulty of knowing that nobody more experienced than Veronica and I would be checking our work as closely. So there was more pressure, but it just meant that we scrutinized our work more closely, which only added to the learning experience.
• What lesson will you take away from this project that you will still use a year from now?
  • A deeper understanding of the scientific principles behind microarray analysis, particularly with respect to how it is processed. While I may not be carrying out microarray analyses upon graduating from LMU (and thus graduating from the Dahlquist Lab), it seems like this technology will become even more pervasive within the field of biology, so and understanding of its inner-workings will be useful. Who knows? It may even become a standard method in medicinal science!

Links

Weekly Group Assignments	Shared Group Journals	Project Links	Team Members
Week 10 Week 11 Week 12 Week 14 Week 15	GÉNialOMICS Week 10 GÉNialOMICS Week 11 GÉNialOMICS Week 12 GÉNialOMICS Week 14 GÉNialOMICS Week 15	GENialOMICS Home Page GENialOMICS Deliverables Gene Database Project Overview Instructions for Project Deliverables Project Manager Guild Page GenMAPP User Guild Page Quality Assurance Guild Page Coder Guild Page	Project Manager/Coder: Anu Varshneya Quality Assurance: Brandon Litvak GenMAPP User: Veronica Pacheco GenMAPP User: Kevin Wyllie

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