# Kristen M. Horstmann Week 12 Journal

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- Reminder: Kara will be analyzing ZAP1 and I will be analyzing wild type.
- Notes on methods, difficulties, and results will be discussed throughout the e-notebook

### 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 which grouped genes based on similarity of gene expression changes over time. The idea 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.

The steps:

- Opened the gene list in Excel for the profile/cluster analyzed for the Week 11 Assignment.
- Copied list of gene IDs onto clipboard

- Launched a web browser, go to the YEASTRACT database.
- On the left panel of the window, clicked on the link to
*Rank by TF*. - Pasted list of genes from cluster into the box labeled
*ORFs/Genes*. - Checked the box for
*Check for all TFs*. - Accepted the defaults for the Regulations Filter (Documented, DNA binding plus expression evidence)
- Did
apply a filter for "Filter Documented Regulations by environmental condition".**not** - Ranked genes by TF using: The % of genes in the list and in YEASTRACT regulated by each TF.
- Clicked the
*Search*button.

- On the left panel of the window, clicked on the link to
- 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?**- You and your partner will need to analyze the same gene regulatory network for your modeling project. Compare the lists of "significant" factors that you and your partner generated.
**How many of the transcription factors appear in both of your lists?**

- Use these transcription factors and add CIN5, GLN3, HMO1, and ZAP1 if they are not in the list. Use discretion to add transcription factors until you reach a list of 15-30 factors.
- Go back to the YEASTRACT database and follow the link to
*Generate Regulation Matrix*. - Copied and pasted 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" - Clicked the "Generate" button.
- In the results window that appears, clicked 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".

- For the first one, accept the defaults: "Documented", "DNA binding

- 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".

### Analyzing and Visualizing Your Gene Regulatory Networks

We will analyze the regulatory matrix files generated above in Microsoft Excel and visualize them using GRNsight to determine which one will be appropriate to pursue further in the modeling.

- First we must properly format the output files from YEASTRACT. Repeat these steps for each of the three files you generated above.
- Opened 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, Selected the entire Column A, go to the "Data" tab and selected "Text to columns". In the Wizard that appears, selected "Delimited" and clicked "Next". In the next window, selected "Semicolon", and clicked "Next". In the next window, left the data format at "General", and clicked "Finish". Should 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.
- Saved this file in Microsoft Excel workbook format (.xlsx).
- Checked 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.
- For this adjacency matrix to be usable in GRNmap (the modeling software) and GRNsight (the visualization software), must transpose the matrix. Inserted a new worksheet into your Excel file and named it "network". Went back to the previous sheet and selected the entire matrix and copied it. In the new worksheet clicked on the A1 cell in the upper left. Selected "Paste special" from the "Home" tab. In the window that appears, checked the box for "Transpose". This pasted 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, deleted the "p" from each of the gene names in the columns. Adjusted the case of the labels to make them all upper case.
- In cell A1, copied and pasted the text "rows genes affected/cols genes controlling".

- Now we will look at some of the network properties. Again, repeated these steps for each of the three gene regulatory matrices you generated above.
- Created a new worksheet and called it "degree". Coped and pasted adjacency matrix from the "network" sheet into this new worksheet.
- In the first empty cell in column A, typed "Out-degree". In the cell to the right of that in Column B, typed the equation
`=SUM(`

and selected the range of cells in column B that has 1's and 0's in it, closed the parentheses, and pressed Enter. This quantity is the number of genes that the transcription factor in that column is controlling, or the out-degree. Copied and pasted that equation across all of the columns. - In Cell 1 of the first empty column to the right of the adjacency matrix, typed "In-degree". In Cell 2 of this column, typed the equation
`=SUM(`

and selected the entire row of 1's and 0's, closed the parentheses, and pressed Enter. This quantity is the number of transcription factors that regulate the gene in that row, or the in-degree. Copied and pasted 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. Ideally, 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, created three columns to the right called "Frequency", "In-degree total", and "Out-degree total". In the "Frequency" column, numbered sequentially from 1 to the largest degree number in your calculations above. In the "In-degree total" column, typed the number of genes with that in-degree for each of the frequencies. In the "Out-degree total" column, typed the number of genes with that out-degree for each of the frequencies.
- Selected the "Frequency", "In-degree total", and "Out-degree total" columns. Went to the "Insert" tab and select the column chart type to insert a plot of the degree distribution. Copied and pasted the charts for each gene regulatory matrix into your PowerPoint presentation.

- Now we 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).
- Selected 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 there's a satisfying layout and take a screenshot of the results. Pasted it into the PowerPoint presentation. Repeated with the other two regulation matrix files.

- Wrote a paragraph discussing and explained the results of each aspect of work.
- Determinined candidate transcription factors that regulated a cluster of genes from your dataset.
- Created three candidate gene regulatory networks.
- Determined the total number of edges and degree distribution of the three gene regulatory networks.
- Visualized the networks.
- Chose a particular gene regulatory network to pursue for the modeling.

#### YEASTRACT Results and Discussion

- 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.
- Notes:
- After pruning, the matrix with Binding AND expression only had 7 transcription factors. I continued on to find the edges but doubt this can be used in the future
- After pruning the ONLY DNA binding matrix, 20 transcription factors remained
- No pruning was needed for matrix binding PLUS expression

- Notes:

- 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.

##### Profile 28

**How many transcription factors are green or "significant"?**- Only one TF is significant

**List the "significant" transcription factors on your wiki page, along with the corresponding "% in user set", "% in YEASTRACT", and "p value".**- Arr1p is significant
- 41.05% user set
- 2.44% Yeastract
- P-value: 0.000014729208160

**Are CIN5, GLN3, HMO1, and ZAP1 on the list?**- No

- Profile 28 was deemed unacceptable due to the lack of significant transcription factors. The rest of the experiment will be continues with Profile 45.

##### Profile 45

**How many transcription factors are green or "significant"?**- 24

**List the "significant" transcription factors on your wiki page, along with the corresponding "% in user set", "% in YEASTRACT", and "p value".**- Media: HorstmannProfile45YEASTRACT.xlsx
- Excel table for the 25 showing the percentages (both user and YEASTRACT) and the p-value.

**Are CIN5, GLN3, HMO1, and ZAP1 on the list?**- No, they have been deleted from the student data

- You and your partner will need to analyze the same gene regulatory network for your modeling project. Compare the lists of "significant" factors that you and your partner generated.
**How many of the transcription factors appear in both of your lists?**- We had 21 shared transcription factors. Sfp1p, Yhp1p, Yox1p, Fkh2p, Cyc8p, YLR278C, Rif1p, ACE2p, Msn2p, Cse2p, Stb5p, Ndt80p, Asg1p, Snf2p, Swi5p, Mig2p,Spt20p,Snf6p, Pdr1p, Dcr2p, Gat3p. We will need to add CIN5, GLN3, HMO1, and ZAP1.

- 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.
- We included all shared transcription factors and the four added ones- CIN5, GLN3, HMO1, ZAP1- as we had an amount that fell within the suggested range.

#### Analyzing and Visulaizing Results and Discussion

- 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.
- Notes:
- After pruning, the matrix with Binding AND expression only had 7 transcription factors. I continued on to find the edges but doubt this can be used in the future
- After pruning the ONLY DNA binding matrix, 20 transcription factors remained
- No pruning was needed for matrix binding PLUS expression

- Notes:
- 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.
- Documented Binding PLUS Expression: 141 edges
- Documented Binding AND Expression: 5 edges
- Documented DNA Binding ONLY: 35 edges
- Due to the above parameters, DNA binding ONLY will probably be used further as it has the edges closest to 40-50

- Frequency, totals, and charts can be found in following excel documents:

- 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.

#### Powerpoint

#### Paragraph

- 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.
- Determining an initial cluster of transcriptional factors was a little more difficult than initially anticipated. The profile I chose to analyze last week, Profile 28, ended up only having 1 significant transcription factor. Therefore, I had to change profiles to the profile that exported the most amount of significant transcriptional factors: Profile 45. Incidentally, this is the same profile number Kara chose to analyze. Nest, we created three gene regulator networks: DNA binding plus expression evidence, DNA binding and expression evidence, and only DNA binding. These had to pruned down to show only the connecting genes, and resulted in less transcriptional factors for two of the networks. The total amount of edges for binding plus expression, binding and expression, and DNA binding only was 141, 5, and 35, respectfully. The degree distribution was hard to determine for the largest and smallest edges, as the smallest only went up to 2, and the largest went up to 22 but with very little to no data after 11. For the data with 35 edges, showing only the DNA binding, I thought the data would've been more even, but only 1 and 2 had both in-degree data and out-degree data, the rest were split or had no data for either. Visualizing the networks using GRNsight was very easy and user-friendly. However, I titled my network sheets as “Network” with a capital N, so I had to go through each one first and amend that error. Also, I experienced some difficulty when I read the instructions to move the genes to the same relative site, as my numbers ranged from 5 to 35 to 141 so it was hard to arrange them very similarly. I will likely be using the “DNA binding only” as it has the number of edges closest to the range Dr. Dahlquist and Dr. Fitzpatrick suggested to us.

### Template

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