Msamdars Week 9

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Purpose

Methods/Results

  1. Why did you select this profile? In other words, why was it interesting to you?
      • I chose this profile because I saw the trend was very consistent. There was a clear trend of gene repression, followed by a period where gene expression was idled, followed by the last period of increased gene expression. I think that this is interesting because we can gain a clearer picture of what genes are activated initially, then repressed during cold shock.
  2. How many genes belong to this profile?
      • 78 genes belong to this profile.
  3. How many genes were expected to belong to this profile?
      • 47.1 genes were expected to belong to this profile.
  4. What is the p value for the enrichment of genes in this profile?
      • The p-value is 0.000016 (1.6 x 10^-5).
    1. How many GO terms are associated with this profile at p < 0.05?
        • There are 8 terms
    2. How many GO terms are associated with this profile with a corrected p value < 0.05?
        • There are no terms.
  5. Look up the definitions for each of the terms at http://geneontology.org. In your research presentation, you will discuss the biological interpretation of these GO terms. In other words, why does the cell react to cold shock by changing the expression of genes associated with these GO terms? Also, what does this have to do with the transcription factor being deleted (for the groups working with deletion strain data)?
      1. GO:0003700
      2. GO:0045944
      3. GO:0055114
      4. GO:0016491
      5. GO:0005783
      6. GO:0046872
  6. How many transcription factors are green or "significant"?
      • 3 are green or "significant".
  7. Are CIN5, GLN3, and/or HAP4 on the list? If so, what is their "% in user set", "% in YEASTRACT", and "p value".
    • Gene % in user set % in YEASTRACT p-value
      CIN5 12.82% 0.46% 0.999791975780520
      GLN3 38.46% 1.24% 0.158609557879648
      HAP4 24.36% 1.74% 0.011729257462495
  8. Explain how you chose the transcription factors?
      • I chose the transcription factors based on the YEASTRACT rankings. Furthermore, I also added the genes that we were told to add: HAP4, CIN5, & GLN3.

Data & Files

dCin5_MS Spreadsheet

Gene & Go Lists .zip File

dCIN5_MS Powerpoint

dCIN5_Profile9_MS GRNsight Input Spreadsheet

dCIN5_MS Stem Input Tab-Delimited Text File

Conclusion

This week, we learned about using the STEM clustering software to identify clusters of genes in our deletion strain that have similar patterns of expression. This week, I chose to look at profile 9 of the CIN5 deletion strain yeast. This profile contains 78 genes with a p-value of 0.000016, a very significant value. I chose this pattern of gene expression because it shows a fairly regular pattern of expression - down-regulation, no change, and then up-regulation. The gene ontology terms have largely to do with DNA transcription and regulation of gene expression. This makes sense because a cell will likely decrease gene expression during environmental stress to focus on self-preservation.

Acknowledgments

This week my team members included Emma Young, Ivy-Quynh Macaraeg, & DeLisa Madere. We worked in class together.

Except for what is noted above, this individual journal entry was completed by me and not copied from another source.

Msamdars (talk) 19:03, 30 October 2019 (PDT)

Other Links

Links
Mihir Samdarshi User Page
Assignment Pages Personal Journal Entries Shared Journal Entries
Week 1 Journal Week 1 Class Journal Week 1
Week 2 Journal Week 2 Class Journal Week 2
Week 3 FAS2/YPL231W Week 3 Class Journal Week 3
Week 4 Journal Week 4 Class Journal Week 4
Week 5 Database - AmtDB Class Journal Week 5
Week 6 Journal Week 6 Class Journal Week 6
Week 7 Journal Week 7 Class Journal Week 7
Week 8 Journal Week 8 Class Journal Week 8
Week 9 Journal Week 9 Class Journal Week 9
Assignment Pages Personal Journal Entries
Week 10 Journal Week 10
Week 11 Journal Week 11
Week 12/13 Journal Week 12/13
Team Project Links
Skinny Genes Team Page

References

  1. LMU BioDB 2019. (2019). Week 9. Retrieved October 29, 2019, from https://xmlpipedb.cs.lmu.edu/biodb/fall2019/index.php/Week_9
  2. Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., ... & Harris, M. A. (2000). Gene ontology: tool for the unification of biology. Nature genetics, 25(1), 25.
  3. Gene Ontology Consortium. (2018). The gene ontology resource: 20 years and still GOing strong. Nucleic acids research, 47(D1), D330-D338.
  4. MediaWiki (2019). Category: Help. Retrieved October 29, 2019, from https://www.mediawiki.org/wiki/Category:Help
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