BIOL398-03/S13:Week 12

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# What aspect of this assignment was the most challenging for you?
# What aspect of this assignment was the most challenging for you?
# What (yet) do you not understand?  
# What (yet) do you not understand?  
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# For this week's and last week's assignment we computed (or the software did) three different p values:  per individual gene, per profile, and per Gene Ontology term.  State in your own words what we need each of these p values for and what are they telling us?
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# For the week 9 and 12 assignments we computed (or the software did) three different p values:  per individual gene, per profile, and per Gene Ontology term.  State in your own words what we need each of these p values for and what are they telling us?
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Revision as of 15:51, 29 March 2013

BIOL398-03: Biomathematical Modeling

MATH 388-01: Survey of Biomathematics

Loyola Marymount University

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This journal entry is due on Tuesday, April 12 at midnight PDT (Monday night/Tuesday morning). NOTE new due date and that the server records the time as Eastern Daylight Time (EDT). Therefore, midnight will register as 03:00.

Contents

Individual Journal Assignment

  • Store this journal entry as "username Week 12" (i.e., this is the text to place between the square brackets when you link to this page).
  • Create the following set of links. (HINT: you can do all of this easily by adding them to your template and then using the template on your pages.)
    • Link to your journal entry from your user page.
    • Link back from your journal entry to your user page.
    • Link to this assignment from your journal entry.
    • Don't forget to add the "BIOL398-03/S13" category to the end of your wiki page.

Background

This is a list of steps required to analyze DNA microarray data.

  1. Quantitate the fluorescence signal in each spot
  2. Calculate the ratio of red/green fluorescence
  3. Log transform the ratios
  4. Normalize the ratios on each microarray slide
    • Steps 1-4 are performed by the GenePix Pro software.
    • You will perform the following steps:
  5. Normalize the ratios for a set of slides in an experiment
  6. Perform statistical analysis on the ratios
  7. Compare individual genes with known data
    • Steps 5-7 are performed in Microsoft Excel
  8. Pattern finding algorithms (clustering)
  9. Map onto biological pathways
    • We will use software called STEM for the clustering and mapping
    • We will use the YEASTRACT Database to determine which transcription factors are likely to regulate the genes in our clusters.
  10. Create mathematical model of transcriptional network

We will perform steps 8-9 this week.

Clustering and Gene Ontology Analysis with STEM

For this assignment, keep an electronic lab notebook recording all of the actions that you take following the protocol. In addition, answer the questions embedded in the protocol.

  1. Begin by downloading and extracting the STEM software. Click here to go to the STEM web site.
    • Click 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.cmd to launch the STEM program.
      • In Seaver 120, we encountered an issue where the program would not launch on the Windows XP machines due to a lack of memory. To get around this problem, launch STEM from the command line.
        • Go to the start menu and click on Programs > Accessories > Command Prompt.
        • You will need to navigate to the directory (folder) in which the STEM program resides. If you followed the instructions above and extracted the stem folder to the Desktop, type the following: cd Desktop\stem and press "Enter".
        • To launch the program then type: java -mx512M -jar stem.jar -d defaults.txt and press "Enter". This will launch the program with less memory allocated to it.
  2. Prepare your microarray data file for loading into STEM.
    • Using the Excel spreadsheet that you turned in for your Week 9 Assignment, insert a new worksheet and name it "stem".
    • Select all of the data from your "final" worksheet and paste it into your "stem" worksheet.
      • Your leftmost column should have the column header "MasterIndex". Rename this column to "SPOT". Column B should be named "ID". Rename this column to "Gene Symbol".
      • Delete all of the data columns EXCEPT for the AvgLogFC columns for each timepoint.
      • Rename the data columns with just the time and units. Use the same timepoints for all samples. For example, 15m, 30m, etc. for the Dahlquist lab data or 0.17h, 0.5h, etc. for the Schade data (for the Schade data, leave out the 0 timepoint).
      • Save your work. Then use Save As to save this spreadsheet as Text (Tab-delimited) (*.txt). Click OK to the warnings and close your file.
  3. Running STEM
    1. In section 1 (Expression Data Info) of the the main STEM interface window, click on the Browse... button to navigate to and select your file.
      • Click on the radio button No normalization/add 0.
      • Check the box next to Spot IDs included in the data file.
    2. In section 2 (Gene Info) of the main STEM interface window, select Saccharomyces cerevisiae (SGD), from the drop-down menu for Gene Annotation Source. Select No cross references, from the Cross Reference Source drop-down menu. Select 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:", click on the radio button that says "Based on real time". Then close the Interface Options window.
      • Take 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 new PowerPoint presentation to save your figures.
    2. Click on each of the profiles to open a window showing a more detailed plot containing all of the genes in that profile.
      • Take 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, click 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. Make your filename descriptive of the contents, e.g. "wt_profile#_genelist.txt", where you replace the number symbol with the actual profile number.
        • Upload this file to LionShare and provide a link to Dr. Dahlquist and Dr. Fitzpatrick.
      • For each of the profiles, click on the "Profile GO Table" to see the list of Gene Ontology terms belonging to the profile. In the window that appears, click on the "Save Table" button and save the file to your desktop. Make your filename descriptive of the contents, e.g. "wt_profile#_GOlist.txt", where you use "wt", "dGLN3" or "Schade" to indicate the dataset and where you replace the number symbol with the actual profile number. At this point you have saved all of the primary data from the STEM software and it's time to interpret the results!
        • Upload this file to LionShare and provide a link to Dr. Dahlquist and Dr. Fitzpatrick.
  5. Analyzing and Interpreting STEM Results
    1. Select one of the profiles you saved in the previous step for further intepretation of the data. We suggest that you choose one that has a pattern of up- or down-regulated genes at the early (first three) timepoints. Answer the following:
      • Why did you select this profile? In other words, why was it intersting to you?
      • How many genes belong to this profile?
      • How many genes were expected to belong to this profile?
      • What is the p value for the enrichment of genes in this profile? Bear in mind that in the week 9 assignment, you computed p values to determine whether each individual gene had a significant change in gene expression at each time point. This p value determines whether the number of genes that show this particular expression profile across the time points is significantly more than expected.
      • Open the GO list file you saved for this profile in Excel. This list shows all of the Gene Ontology terms that are associated with genes that fit this profile. Select the third row and then choose 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. How many GO terms are associated with this profile at p < 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. How many GO terms are associated with this profile with a corrected p value < 0.05?
      • Select 10 Gene Ontology terms from your filtered list (either p < 0.05 or corrected p < 0.05). Look up the definitions for each of the terms at http://geneontology.org. Write a paragraph that describes 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?

Using YEASTRACT to Infer which Transcription Factors Regulate a Cluster of Genes

In the previous analysis using STEM, we found a number of gene expression profiles (aka clusters) which grouped genes based on similarity of gene expression changes over time. The implication is that these genes share the same expression pattern because they are regulated by the same (or the same set) of transcription factors. We will explore this using the YEASTRACT database.

  1. Open the gene list in Excel for the profile/cluster that you analyzed above.
    • Copy the list of gene IDs onto your clipboard.
  2. Launch a web browser and go to the YEASTRACT database.
    • On the left panel of the window, click on the link to group by TF.
    • Paste your list of genes from your cluster into the box labeled ORFs/Genes.
    • Check the box for Check for all TFs.
    • Uncheck the box for Indirect Evidence.
    • Click the Search button.
  3. Answer the following questions:
    • What are the top 10 transcription factors in your results? List them on your wiki page with the percent of the genes in your cluster that they each regulate.
    • Is Gln3 on the list? What percentage of the genes in the cluster does it regulate? How many genes does it regulate? What are the names of the genes?
  4. For the mathematical model that we will build in class, we need to define a gene regulatory network of transcription factors that regulate other transcription factors. We can use YEASTRACT to assist us with creating the network. The model that we will start with has the following transcription factors in it:
CIN5
CUP9
FHL1
GTS1
HSF1
MSN1
MSN4
NRG1
RAP1
RCS1
REB1
ROX1
RPH1
YAP1
YAP6
  • We will also include GLN3 because it is known to regulate the genes that code for enzymes in nitrogen metabolism. Based on your previous analysis of the transcription factors that regulate your chosen cluster above, select up to five additional transcription factors to add to the network. Which transcription factors do you want to add to the model and why?
  • Go back to the YEASTRACT database and follow the link to Generate Regulation Matrix.
    • Copy and paste the list above, plus GLN3, plus the additional transcription factors you identified into both the "Transcription Factor" field and the "ORF/Genes" field.
    • Uncheck the box for "Indirect Evidence" and select "JPEG" from the drop-down menu for the "Output Image".
    • Click the "Generate" button.
  • In the results window that appears, click on the link to the "RegulationMatrix" file that appears and save it to your Desktop.
  • Click on the "Image" link to see the diagram of the network. Save the image file (you can copy and paste it to your PowerPoint file or upload it to the wiki).
  • We will use this matrix file as an input to your model next week.
  • Make sure that your wiki assignment page includes your PowerPoint file to which you saved your screenshots and your "RegulationMatrix" file.

Shared Journal Assignment

  • Store your journal entry in the shared Class Journal Week 12 page. If this page does not exist yet, go ahead and create it (congratulations on getting in first :) )
  • Link to your journal entry from your user page.
  • Link back from the journal entry to your user page.
  • Sign your portion of the journal with the standard wiki signature shortcut (~~~~).
  • Add the "BIOL398-03/S13" category to the end of the wiki page (if someone has not already done so).

Reflection

  1. What aspect of this assignment came most easily to you?
  2. What aspect of this assignment was the most challenging for you?
  3. What (yet) do you not understand?
  4. For the week 9 and 12 assignments we computed (or the software did) three different p values: per individual gene, per profile, and per Gene Ontology term. State in your own words what we need each of these p values for and what are they telling us?
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