Tessa A. Morris Week 12

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Electronic Lab Notebook






Alyssa N Gomes


Use the YEASTRACT database to explore the implication that genes grouped by similarity of gene expression changes over time share the same expression pattern because they are regulated by the same (or the same set) of transcription factors and use GRNsight to determine which regulatory matrix files should be pursued further in the modeling.


  1. Turn on file extension:"Control Panel" > "Folder options" > "View" > Uncheck "Hide extensions for known file types" > OK

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

  1. Open the gene list of study in Excel:
    • Download the Gene Table for profile 45 (from lionshare)
    • Go to Excel > Open and change the setting to "All File Types" and select profile 45
    • Copy the list of gene IDs onto your clipboard.
  2. Go to the YEASTRACT database.
    • Click Rank by TF in the top left hand corner
    • Paste your list of genes from your cluster into the box labeled ORFs/Genes.
      • Use the excel trick "control, shift, down arrow" to select all genes
    • Check the box for Check for all TFs.
    • Accept the defaults for the Regulations Filter (Documented, DNA binding plus expression evidence)
    • Do not apply a filter for "Filter Documented Regulations by environmental condition".
    • Rank genes by TF using: The % of genes in the list and in YEASTRACT regulated by each TF.
    • Click the Search button.
  3. Answer the following questions:
    • In the results window that appears, the p values colored green are considered "significant", the ones colored yellow are considered "borderline significant" and the ones colored pink are considered "not significant". How many transcription factors are green or "significant"?
    • List the "significant" transcription factors on your wiki page, along with the corresponding "% in user set", "% in YEASTRACT", and "p value".
      • Are CIN5, GLN3, HMO1, and ZAP1 on the list? (If they aren't add them)
  4. We want to generate a network with approximately 15-30 transcription factors in it.
    • Alyssa N Gomes and I need to analyze the same gene regulatory network for your modeling project. We chose profile 45
    • Compare the lists of "significant" factors and answer the question: How many of the transcription factors appear in both of your lists? You will use these transcription factors and add CIN5, GLN3, HMO1, and ZAP1 if they are not in your list. Use your discretion to add transcription factors until you reach a list of 15-30 factors. Explain in your electronic notebook how you decided on which transcription factors to include. Record the list and your justification in your electronic lab notebook.
    • Go back to the YEASTRACT database and follow the link to Generate Regulation Matrix.
    • Copy and paste the list of transcription factors you identified (plus CIN5, GLN3, HMO1, and ZAP1) into both the "Transcription factors" field and the "Target ORF/Genes" field.
    • We are going to generate several regulation matrices, with different "Regulations Filter" options.
      • For the first one, accept the defaults: "Documented", "DNA binding plus expression evidence"
      • Click the "Generate" button.
      • In the results window that appears, click on the link to the "Regulation matrix (Semicolon Separated Values (CSV) file)" that appears and save it to your Desktop. Rename this file with a meaningful name so that you can distinguish it from the other files you will generate.
      • Repeat these steps to generate a second regulation matrix, this time applying the Regulations Filter "Documented", "Only DNA binding evidence".
      • Repeat these steps a third time to generate a third regulation matrix, this time applying the Regulations Filter "Documented", DNA binding and expression evidence".

Analyzing and Visualizing Your Gene Regulatory Networks

  1. Format the output files from YEASTRACT (repeat these steps for each of the three file generated above)
    • Open the file in Excel. It will not open properly in Excel because a semicolon was used as the column delimiter instead of a comma. To fix this, Select the entire Column A. Then go to the "Data" tab and select "Text to columns". In the Wizard that appears, select "Delimited" and click "Next". In the next window, select "Semicolon", and click "Next". In the next window, leave the data format at "General", and click "Finish". This should now look like a table with the names of the transcription factors across the top and down the first column and all of the zeros and ones distributed throughout the rows and columns. This is called an "adjacency matrix." If there is a "1" in the cell, that means there is a connection between the trancription factor in that row with that column.
    • Save this file in Microsoft Excel workbook format (.xlsx).
    • Check to see that all of the transcription factors in the matrix are connected to at least one of the other transcription factors by making sure that there is at least one "1" in a row or column for that transcription factor. If a factor is not connected to any other factor, delete its row and column from the matrix. Make sure that you still have somewhere between 15 and 30 transcription factors in your network after this pruning.
    • For this adjacency matrix to be usable in GRNmap (the modeling software) and GRNsight (the visualization software), we need to transpose the matrix. Insert a new worksheet into your Excel file and name it "network". Go back to the previous sheet and select the entire matrix and copy it. Go to you new worksheet and click on the A1 cell in the upper left. Select "Paste special" from the "Home" tab. In the window that appears, check the box for "Transpose". This will paste your data with the columns transposed to rows and vice versa. This is necessary because we want the transcription factors that are the "regulatORS" across the top and the "regulatEES" along the side.
    • The labels for the genes in the columns and rows need to match. Thus, delete the "p" from each of the gene names in the columns. Adjust the case of the labels to make them all upper case.
    • In cell A1, copy and paste the text "rows genes affected/cols genes controlling".
  2. Now we will look at some of the network properties. Again, repeat these steps for each of the three gene regulatory matrices you generated above.
    • Create a new worksheet and call it "degree". Copy and paste your adjacency matrix from the "network" sheet into this new worksheet.
    • In the first empty cell in column A, type "Out-degree". In the cell to the right of that in Column B, type the equation =SUM( and select the range of cells in column B that has 1's and 0's in it, close the parentheses, and press Enter. This quantity is the number of genes that the transcription factor in that column is controlling, or the out-degree. Copy and paste that equation across all of the columns.
    • In Cell 1 of the first empty column to the right of the adjacency matrix, type "In-degree". In Cell 2 of this column, type the equation =SUM( and select the entire row of 1's and 0's, close the parentheses, and press Enter. This quantity is the number of transcription factors that regulate the gene in that row, or the in-degree. Copy and paste the equation down the entire column, including the row that contains the out-degree sums.
    • The number in the lower right-hand corner, the sum of sums, is the total number of edges in the adjacency matrix. We would like to see about 50 (40-60 or so) edges in the matrix. If the matrix is too dense, it will slow down the modeling program because it will be difficult to estimate the parameters in the model.
    • We want to plot the degree distributions for each of your gene regulatory networks. In the "degree" worksheet, create three columns to the right called "Frequency", "In-degree total", and "Out-degree total". In the "Frequency" column, number sequentially from 1 to the largest degree number in your calculations above. In the "In-degree total" column, type the number of genes with that in-degree for each of the frequencies. In the "Out-degree total" column, type the number of genes with that out-degree for each of the frequencies.
    • Select the "Frequency", "In-degree total", and "Out-degree total" columns. Go to the "Insert" tab and select the column chart type to insert a plot of the degree distribution. Copy and paste the charts for each gene regulatory matrix into your PowerPoint presentation.
  3. Now we will visualize what these gene regulatory networks look like with the GRNsight software.
    • Go to the GRNsight home page (you can either use the version on the home page or the beta version, which has slightly different visualization properties).
    • Select the menu item File > Open and select one of the regulation matrix .xlsx file that has the "network" worksheet in it that you formatted above. If the file has been formatted properly, GRNsight should automatically create a graph of your network. Move the nodes (genes) around until you get a layout that you like and take a screenshot of the results. Paste it into your PowerPoint presentation. Repeat with the other two regulation matrix files. You will want to arrange the genes in the same order for each screenshot so that the graphs can be easily compared.
  4. Write a paragraph discussing and explaining the results of each aspect of today's work.
    • Determining candidate transcription factors that regulate a cluster of genes from your dataset.
    • Creating three candidate gene regulatory networks.
    • Determining the total number of edges and degree distribution of your three gene regulatory networks.
    • Visualizing the networks.
    • Choosing a particular gene regulatory network to pursue for the modeling.

Data & Observations:

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

  1. There are 28 transcription factors that are green or "significant"
  2. "Significant" transcription factors:
    •  Sfp1p
      • % in user set: 81.27%
      • % in Yeastract: 6.81%
      • p-value: 0.000000000000000
    •  Msn2p
      • % in user set: 68.87%
      • % in Yeastract: 7.33%
      • p-value: 0.000000000000000
    •  Yhp1p
      • % in user set: 44.35%
      • % in Yeastract: 12.51%
      • p-value: 0.000000000000000
    •  Yox1p
      • % in user set: 45.18%
      • % in Yeastract: 11.48%
      • p-value: 0.000000000000000
    •  Cyc8p
      • % in user set: 0.55%
      • % in Yeastract: 100.00%
      • p-value: 0.000000000000000
    •  Rif1p
      • % in user set: 15.43%
      • % in Yeastract: 15.64%
      • p-value: 4E-15
    •  Fkh2p
      • % in user set: 23.14%
      • % in Yeastract: 12.03%
      • p-value: 5E-15
    •  Cse2p
      • % in user set: 24.24%
      • % in Yeastract: 11.34%
      • p-value: 3.5E-14
    •  Ace2p
      • % in user set: 82.37%
      • % in Yeastract: 6.26%
      • p-value: 1.109E-11
    •  Pdr1p
      • % in user set: 34.16%
      • % in Yeastract: 8.62%
      • p-value: 6.5465E-11
    •  Swi5p
      • % in user set: 41.87%
      • % in Yeastract: 7.82%
      • p-value: 3.43909E-10
    •  Stb5p
      • % in user set: 28.37%
      • % in Yeastract: 8.86%
      • p-value: 1.12448E-09
    •  Asg1p
      • % in user set: 10.19%
      • % in Yeastract: 14.45%
      • p-value: 1.76512E-09
    •  Snf6p
      • % in user set: 51.79%
      • % in Yeastract: 7.12%
      • p-value: 2.2267E-09
    •  Snf2p
      • % in user set: 43.25%
      • % in Yeastract: 7.54%
      • p-value: 2.44326E-09
    •  Mig2p
      • % in user set: 11.29%
      • % in Yeastract: 13.36%
      • p-value: 2.97316E-09
    •  YLR278C
      • % in user set: 13.77%
      • % in Yeastract: 11.76%
      • p-value: 5.53632E-09
    •  Snf5p
      • % in user set: 31.13%
      • % in Yeastract: 8.23%
      • p-value: 1.20634E-08
    •  Gcr2p
      • % in user set: 29.75%
      • % in Yeastract: 8.26%
      • p-value: 2.44706E-08
    •  Msn4p
      • % in user set: 49.59%
      • % in Yeastract: 7.02%
      • p-value: 2.70877E-08
    •  Spt20p
      • % in user set: 39.94%
      • % in Yeastract: 7.49%
      • p-value: 2.90461E-08
    •  Tup1p
      • % in user set: 50.41%
      • % in Yeastract: 6.88%
      • p-value: 9.54855E-08
    •  Spt2p
      • % in user set: 11.57%
      • % in Yeastract: 10.88%
      • p-value: 8.10535E-07
    •  Zap1p
      • % in user set: 31.68%
      • % in Yeastract: 7.52%
      • p-value: 1.44152E-06
    •  Ndt80p
      • % in user set: 14.33%
      • % in Yeastract: 9.15%
      • p-value: 9.67532E-06
    •  Mcm1p
      • % in user set: 32.78%
      • % in Yeastract: 7.13%
      • p-value: 1.41637E-05
    •  Ino4p
      • % in user set: 18.73%
      • % in Yeastract: 8.17%
      • p-value: 2.2768E-05
    •  Rlm1p
      • % in user set: 16.53%
      • % in Yeastract: 8.44%
      • p-value: 2.70863E-05
  3. CIN5, GLN3, and HMO1 are not on the list, but ZAP1 is
  4. 22 transcription factors appeared on both lists. They were:
    •  Ace2p
    •  Asg1p
    •  Cse2p
    •  Cyc8p
    •  Fkh2p
    •  Gcr2p
    • Mcm1p
    •  Mig2p
    •  Msn2p
    •  Msn4p
    •  Ndt80p
    •  Pdr1p
    •  Rif1p
    •  Sfp1p
    •  Snf2p
    •  Snf6p
    •  Spt20p
    •  Stb5p
    •  Swi5p
    •  Yhp1p
    •  YLR278C
    •  Yox1p
  5. The profiles that Alyssa and I chose already had 15-30 transcription factors so we did not need to add anymore.
  6. Discussing the results of each aspect of today's work:
    • When choosing which profile to study, Alyssa and I chose the profile that was sorted to have the most significant, profile 45. When the transcription factors were sorted by significance (p < 0.05), 24 of the wild type transcription factors were significant, and 28 of the Δgln3 were significant. Wild type and Δgln3 had 22 significant transcription factors in common. Using YEASTRACT, we created generate regulation matrices for “DNA binding PLUS expression evidence,” “DNA binding AND expression evidence,” and “Only DNA binding evidence.” Because “DNA binding AND expression evidence” required there to be both DNA binding and expression evidence present, there was the least amount of connections shown by the GRNsight maps. The trouble with this map is that there were not 15-30 transcription factors present. Similarly, because “DNA binding PLUS expression evidence” only required there to DNA binding or expression evidence present, there was the most amount of connections shown by the GRNsight maps. Following a similar trend, the “DNA binding PLUS expression evidence” had 185 total number of edges, “DNA binding AND expression evidence” only had 7, and “Only DNA binding evidence” had 51. These numbers were found by finding the out-degree and the in-degree, as explained in the methods section. The desired amount of edges was 40-60, so the “Only DNA binding evidence” was the most optimal for further study. The frequency distribution for “DNA binding AND expression evidence” had very little data, so it is difficult to come to conclusions. The distribution for “DNA binding PLUS expression evidence” was roughly bell-shaped, with the most frequent number being 7. “DNA binding AND expression evidence” had a skewed right plot with it being most common to have a low number of connections.
  7. The updated powerpoint presentation can be found Here
    • For Δgln3 "binding and expression" there were less than 15-30 transcription factors.


  • Alyssa and I corrected the data.
    • Only delete transcription factors that have only zeros in both the columns and the rows.
    • We are working on the same network
    • For my data: delete RLM1 and TUP1 from my data to match Alyssa's data.

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