Dbashour Week 10

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Electronic Notebook

Clustering and GO Term Enrichment with stem

  1. Prepare your microarray data file for loading into STEM.
    • I downloaded my Excel workbook that you I used for my Week 8 assignment.
    • I Inserted a new worksheet into my Excel workbook, and name it "dGLN3_stem".
    • Select all of the data from your "dGLN3_ANOVA" worksheet and Paste special > paste values into my "dGLN3_stem" worksheet.
      • my leftmost column should have the column header "Master_Index". I renamed this column to "SPOT". Column B is named "ID". I renamed this column to "Gene Symbol". I delete the column named "Standard_Name".
      • I 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, I selected all of the rows (except for my header row) and deleted the rows by right-clicking and choosing "Delete Row" from the context menu. I undid the filter. This ensures that I will cluster only the genes with a "significant" change in expression and not the noise. There were 1258 genes left after filtering.
      • I deleted all of the data columns EXCEPT for the Average Log Fold change columns for each timepoint (for example, dGLN3_AvgLogFC_t15, etc.).
      • I renamed the data columns with just the time and units (for example, 15m, 30m, etc.).
      • I saved my work. Then used 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.
  2. Then I download and extracted the STEM software. Click here to go to the STEM web site.
    • I clicked on the download link, register, and download the stem.zip 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 stem. Inside the folder, double-click on the stem.jar to launch the STEM program.
  3. Running STEM
    1. In section 1 (Expression Data Info) of the the main STEM interface window, I clicked on the Browse... button to navigate to and select my file.
      • I clicked on the radio button No normalization/add 0.
      • I checked the box next to Spot IDs included in the data file.
    2. In section 2 (Gene Info) of the main STEM interface window, I selected Saccharomyces cerevisiae (SGD), from the drop-down menu for Gene Annotation Source. I Selected No cross references, from the Cross Reference Source drop-down menu. I selected No Gene Locations from the Gene Location Source drop-down menu.
    3. 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.
    4. In section 4 (Execute) click on the yellow Execute button to run STEM.
  4. Viewing and Saving STEM Results
    1. 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:", I clicked on the radio button that says "Based on real time". Then close the Interface Options window.
      • I took a screenshot of this window (on a PC, simultaneously press the Alt and PrintScreen buttons to save the view in the active window to the clipboard) and paste it into a PowerPoint presentation to save your figures.
    2. I clicked 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.
      • I took 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, I clicked 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. I made my filename descriptive of the contents, e.g. "dGLN3_profile#_genelist.txt", where I replaced the number symbol with the actual profile number.
        • I upload these files to the wiki and link to them on my 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, I clicked on the "Profile GO Table" to see the list of Gene Ontology terms belonging to the profile. In the window that appears, I clicked on the "Save Table" button and saved the file to your desktop. I made my filename descriptive of the contents, e.g. "dGLN3_profile#_GOlist.txt". to indicate the dataset and where I replaced the number symbol with the actual profile number. At this point I have saved all of the primary data from the STEM software and it's time to interpret the results!
        • I 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).
  5. Analyzing and Interpreting STEM Results
    • I selected profile #9 to interpret further. I chose this because this profile had a downward pattern, indicating that gene expression decreased with cold shock treatment.
    • 118.0 genes belong to this profile.
    • 34.1 genes were expected to belong to this profile
    • The p-value for the enrichment of genes in this profile is 1.3E-30 which shows that this expression profile is significantly more than what was expected.
      • I opened the GO list file I saved for this profile in Excel. This list shows all of the Gene Ontology terms that are associated with genes that fit this profile. I Selected the third row and then chose 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. There are 55 GO terms are associated with this profile when a p value < 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. There are 4 GO terms are associated with this profile when a corrected p value < 0.05.
      • I Selected 6 Gene Ontology terms from my filtered list (either p < 0.05 or corrected p < 0.05).
        • To easily look up the definitions, I went to http://geneontology.org.
        • I copied and pasted the GO ID (e.g. GO:0044848) into the search field at center top of the page called "Search GO Data".
        • In the results page, I click on the button that says "Link to detailed information about <term>, in this case "biological phase"". Here are the results:
        • small molecule metabolic process - The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule
        • organic acid catabolic process - The chemical reactions and pathways resulting in the breakdown of organic acids, any acidic compound containing carbon in covalent linkage
        • hydrolase activity - Catalysis of the hydrolysis of various bonds, e.g. C-O, C-N, C-C, phosphoric anhydride bonds, etc. Hydrolase is the systematic name for any enzyme of EC class 3
        • co-enzyme binding - Interacting selectively and non-covalently with a coenzyme, any of various nonprotein organic cofactors that are required, in addition to an enzyme and a substrate, for an enzymatic reaction to proceed
        • cellular response to stress - Any process that results in a change in state or activity of a cell (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a stimulus indicating the organism is under stress. The stress is usually, but not necessarily, exogenous (e.g. temperature, humidity, ionizing radiation)
        • protein modification by small protein conjugation or removal - A protein modification process in which one or more groups of a small protein, such as ubiquitin or a ubiquitin-like protein, are covalently attached to or removed from a target protein

The cell reacts to cold shock by changing expression of genes associated with these go terms in order to survive under stressful conditions. When the cell undergoes stress (i.e. cold shock), it undergoes a change of expression of genes that would be effected by this stressful condition. Specifically in this case, we are looking at if GLN3 was deleted, which is a transcription factor, what effects would it have on the cell. Once this is determined, we would be able to decide the function of GLN3 in yeast and its relation to cold shock.

Summary

For this week, we utilized stem to analyze the response of yeast genes to cold shock. We found the GO list and gene list for each profile. From the multiple profiles we received from stem, I selected profile #9 to interpret further because of it's significance and number of genes associated with this profile. We then found genes and GO terms which were most significant in expression changes during cold shock. I chose 6 of these GO terms and defined them and how they related to cold shock's effect on yeast. Yeast will choose to either down regulate or up regulate a gene's expression based on what the genes effect is and how much energy yeast decides to expend on that gene process when cold shocked.

Deliverable

DGLN3 ANOVA/Stem
DGLN3 ppt Dina
DGLN3 Gene List and GO list

Acknowledgements

  • Zack helped me with performing this assignment and analyzing the data. Dr. Dahlquist provided the data through her research on yeast and cold shock. I referred to the Week 10 wiki page to complete this assignment and modified the instructions as well. I also used the deliverable from my Week 8 individual assignment to complete this week's assignment.

While I worked with the people noted above, this individual journal entry was completed by me and not copied from another source.
Dbashour (talk) 15:09, 6 November 2017 (PST)

References

  1. LMU BioDB 2017. (2017). Week 8. Retrieved October 27, 2017, from https://xmlpipedb.cs.lmu.edu/biodb/fall2017/index.php/Week_8
  2. LMU BioDB 2017. (2017). Week 10. Retrieved October 27, 2017, from https://xmlpipedb.cs.lmu.edu/biodb/fall2017/index.php/Week_10
  3. Consortium, G. O. (n.d.). Small molecule metabolic process. Retrieved December 09, 2017, from http://amigo.geneontology.org/amigo/term/GO:0044281
  4. Consortium, G. O. (n.d.). Organic acid catabolic process. Retrieved December 09, 2017, from http://amigo.geneontology.org/amigo/term/GO:0016054
  5. Consortium, G. O. (n.d.). Hydrolase activity. Retrieved December 09, 2017, from http://amigo.geneontology.org/amigo/term/GO:0016787
  6. Consortium, G. O. (n.d.). Coenzyme binding. Retrieved December 09, 2017, from http://amigo.geneontology.org/amigo/term/GO:0050662
  7. Consortium, G. O. (n.d.). Cellular response to stress. Retrieved December 09, 2017, from http://amigo.geneontology.org/amigo/term/GO:0033554
  8. Consortium, G. O. (n.d.). Protein modification by small protein conjugation or removal. Retrieved December 09, 2017, from http://amigo.geneontology.org/amigo/term/GO:0070647
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