Johnllopez Week 10

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Electronic Lab Notebook

Preparing My Microarray Data File for Loading into STEM

  1. I started this portion by downloading the following spreadsheets. I added a new worksheet named dSWI4_stem, selected the values from dSWI4_ANOVA, and copied them into the new worksheet.
  2. I modified this further by renaming the header "Master_Index" column to "SPOT", "ID" to "Gene Symbol", and deleting the "Standard_Name" column.
  3. I filtered the data on the B-H corrected p-value column to be greater than 0.05, and deleted all the data in the header row. Once I undid the filter, this ensured that all of the genes within the data set would have a B-H corrected p-value of <.05. The result was 2794 genes remaining.
  4. I then deleted all of the columns except for the Average Log Fold change columns at the timepoints. I renamed the columns with just time and units. This would be used for analyzing the timepoints in STEM later on.
  5. In addition, to avoid complications with the STEM software, I replaced any values with the error #DIV/0! with a blank string. There were 40 replacements made.
  6. I saved the spreadsheets as usual, then I saved it as a .txt file, which you can see in this package.

Downloading and Extracting STEM Software / Running STEM

  1. I was able to successfully download the STEM software by going to the following link, downloading/extracting the file, and clicking on the .jar program within it : http://www.cs.cmu.edu/~jernst/stem/
  2. Before I ran the software using the .txt file, I changed several settings. For the expression data info, I uploaded the .txt file, selected "no normalization" and "spot ID's included in file".
  3. In the gene info section, I selected "SGD" for the Gene Annotation Source, "no cross references", and "no gene locations". This ensured that the data would only come from SGD and be specialized for yeast.
  4. Finally, before executing the file, I made sure the clustering method was "STEM Clustering Method".

Viewing and Saving STEM Results

  1. After changing the Interface Options to say "X-axis scale should be based on real time", I took a screenshot of the "All STEM Profiles(1)" window, and placed it into the powerpoint given in the next step.
  2. The following powerpoint contains screenshots of each of the individual colored boxes, which meant that these p-values within that color have a statistically significant number of assigned genes.
  3. This .zip file contains a series of the genes belonging to each individual significant profile.
  4. This .zip file contains a series of the gene ontology terms belonging to each individual significant profile.

Analyzing and Interpreting STEM Results

  1. I chose profile 36 to answer the following questions. I found 36 to be the most interesting because of the drastic expression changes at 30m, 90m, and 120m when the expression changes go from positive, to negative, then to positive again.
  2. 55 genes belong to this profile.
  3. 30.5 genes were expected to belong to this profile.
  4. The p-value for the enrichment of genes is 3.5E-5, or 0.000035.
  5. After filtering the Gene Ontology terms associated with profile 36 to have a p-value > .05, I discovered that 88 of them were associated with it.
  6. After filtering the GO terms associated with profile 36 to have a corrected p-value > .05, I discovered that 133 of them were associated with it.
  7. I then selected the following terms from my filtered list: "regulation of metabolic process", "catalytic activity, acting on RNA", "hydrolase activity, acting on ester bonds", "organelle part", "transcription, DNA-templated", and "response to chemical".

GO Definitions

  • Regulation of Metabolic Process: Any process that modulates the frequency, rate or extent of the chemical reactions and pathways within a cell or an organism.
  • Catalytic Activity, Acting on RNA: Catalytic activity that acts to modify RNA.
  • Hydrolase Activity, Acting on Ester Bonds: Catalysis of the hydrolysis of any ester bond.
  • Organelle Part:Any constituent part of an organelle, an organized structure of distinctive morphology and function. Includes constituent parts of the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, but excludes the plasma membrane.
  • Transcription, DNA-templated:The cellular synthesis of RNA on a template of DNA.
  • Response to Chemical:Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a chemical stimulus.

Conclusion

This week's lab excercise involved analyizng a set of yeast genes and their expression changes to cold shock to show which genes change significantly when exposed to cold shock. To do this, we had to first filter out every gene that had a B-H Modified p-value that was not less than .05. This meant that we would only be looking at the genes that had a significantly low p-value, meaning these genes would genuinely have an expression change most likely to the cold shock. To see which genes had similar patterns in expression changes due to cold shock, I used a software called Short Time-series Expression Miner, or STEM. STEM was easily able to view which genes followed a common model expression profile as well as allow me to view what functions they perform. After running the genes through STEM and having STEM sort them by expression profiles, I saw how genes for certain functions reacted to cold shock. I picked six gene ontology expressions that followed profile 36 for further analysis, which would be "regulation of metabolic process", "catalytic activity, acting on RNA", "hydrolase activity, acting on ester bonds", "organelle part", "transcription, DNA-templated", and "response to chemical". All of these functions followed the same pattern of expression change when given cold shock.

Deliverables in One Convenient Place

Acknowledgements and References

Acknowledgements

I worked with my partner Corrine Wong in class. On Thursday, we discussed how to go through the steps given on the sheet and we made sure we were on track. While I worked with the people noted above, this individual journal entry was completed by me and not copied from another source.

Johnllopez616 (talk) 21:17, 6 November 2017 (PST)

References

LMU BioDB 2017. (2017). Week 10. Retrieved November 6, 2017, from https://xmlpipedb.cs.lmu.edu/biodb/fall2017/index.php/Week_10
Gene Ontology (2017). GO:0019222. Retrieved November 6, 2017, from http://amigo.geneontology.org/amigo/term/GO:0019222
Gene Ontology (2017). GO:0006351. Retrieved November 6, 2017, fromhttp://amigo.geneontology.org/amigo/term/GO:0006351
Gene Ontology (2017). GO:0044422. Retrieved November 6, 2017, fromhttp://amigo.geneontology.org/amigo/term/GO:0044422
Gene Ontology (2017). GO:0016788. Retrieved November 6, 2017, fromhttp://amigo.geneontology.org/amigo/term/GO:0016788
Gene Ontology (2017). GO:0140098. Retrieved November 6, 2017, fromhttp://amigo.geneontology.org/amigo/term/GO:0140098
Gene Ontology (2017). GO:0042221. Retrieved November 6, 2017, fromhttp://amigo.geneontology.org/amigo/term/GO:0042221

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