Austindias Week 4/5

Purpose

The purpose of the first part this assignment was to use Microsoft excel to perform statistical operations, such as p-values to determine significant data. STEM was conducted to group gene expression patterns found throughout the cold shock treatment. From this, a specific profile was chosen to find relationships between different genes and whether they were regulated by the same transcription factors and whether they had similar biological processes. GRNsight was used to model the relationships between the transcription factors involved in the specific profile chosen. Overall, these methods in conjunction work to discover a more wholistic image of how cold shock effects gene expression in terms of biological processes.

Background

Steps to analyze DNA microarray data.

1. Quantitate the fluorescence signal in each spot (Gene Pix Pro Software)
2. Calculate the ratio of red/green fluorescence (Gene Pix Pro Software)
3. Log2 transform the ratios (Gene Pix Pro Software)
4. Normalize the ratios on each microarray slide (using a script in R, a statistics package (see: Microarray Data Analysis Workflow))
5. Normalize the ratios for a set of slides in an experiment (using a script in R, a statistics package (see: Microarray Data Analysis Workflow))
6. Perform statistical analysis on the ratios (Microsoft Excel)
7. Compare individual genes with known data (Microsoft Excel)
8. Use pattern finding algorithms-clustering (STEM software)
9. Map onto biological pathways
10. Identifying regulatory transcription factors responsible for observed changes in gene expression
11. Dynamical systems modeling of the gene regulatory network (MATLAB)

Methods

• Data was shared through an Excel spreadsheet on LMU box by Dr. Dahlquist and Dr. Fitzpatrick
• https://lmu.app.box.com/file/401118899547

Statistical Analysis Part 1: ANOVA

• Created a new worksheet, naming it BIOL388_S19_microarray-data_dGLN3_AD.xlsx
• Copied the first three columns containing the "MasterIndex", "ID", and "Standard Name" from the "Master_Sheet" worksheet for dGLN3.
• At the top of the first column to the right of my data, I created five column headers of the form. dGLN3_AvgLogFC_(TIME) where (TIME) is 15, 30, 60,90, and 120.
• In the cell below the dGLN3_AvgLogFC_t15 header, I typed =AVERAGE(
• Highlighted all the data in row 2 associated with dGLN3 and t15, pressed the closing paren key (shift 0),and pressed the "enter" key.
  • This cell now contains the average of the log fold change data from the first gene at t=15 minutes.
• Clicked on this cell and positioned my cursor at the bottom right corner. Double clicked, and the formula was copied to the entire column of 6188 other genes.
• Repeated steps (4) through (8) with the t30, t60, t90, and the t120 data.
• In the first empty column to the right of the dGLN3_AvgLogFC_t120 calculation, created the column header dGLN3_ss_HO.
• In the first cell below this header, typed =SUMSQ(
• Highlighting all the LogFC data in row 2 for dGLN3 (but not the AvgLogFC), press the closing paren key (shift 0),and press the "enter" key.
• In the next empty column to the right of dGLN3_ss_HO, created the column headers dGLN3_ss_(TIME) as in (3).
• Made a note of how many data points I had at each time point for your strain (It was 4). Count carefully. Also, make a note of the total number of data points(20).
• In the first cell below the header dGLN3_ss_t15, type <=SUMSQ(D80:G80)-COUNTA(D80:G80)*X80^2> and hit enter.
• Repeat Step (7) to copy the formula throughout the column.
• Repeat this computation for the t30 through t120 data points.
  • make sure to get the data for each time point, type the right number of data points, and get the average from the appropriate cell for each time point, and copy the formula to the whole column for each computation.
• In the first column to the right of dGLN3_ss_t120, created the column header dGLN3_SS_full.
• In the first row below this header, typed <=SUM(AD2:AH2)> and hit enter.
• In the next two columns to the right, created the headers dGLN3_Fstat and dGLN3_p-value.
  • Recall the number of data points from (13): call that total n.
• In first cell of the dGLN3_Fstat column, <=((20-5)/5)*(AC2-AI2)/AI2> and hit enter.
  • note that "5" is the number of timepoints
• Copy to the whole column.
• In the first cell below the dGLN3_p-value header, type <=FDIST(AJ2,5,20-5)>
  • "n" is the same as in (13) with the number of data points total.
• Copy to the whole column.
• performed a quick sanity check to ensure I did all of these computations correctly.
• Clicked on the cell A1 and clicked on the Data tab. Selected the Filter icon (looks like a funnel). Little drop-down arrows should appear at the top of each column. This enabled me to filter the data according to criteria that was set.
• Clicked on the drop-down arrow for the dGLN3_p-value column. Selected "Number Filters". In the window that appears,I set a criterion that will filter your data so that the p value has to be less than 0.05.
  • Excel only displays the rows that correspond to data meeting that filtering criterion. A number will appear in the lower left hand corner of the window giving you the number of rows that meet that criterion.

Calculate the Bonferroni and p value Correction

• performed adjustments to the p value to correct for the multiple testing problem. Labeled the next two columns to the right with the same label, dGLN3_Bonferroni_p-value.
• Typed the equation <=AK2*6189>, After completing this computation, use the Step (10) strategy to copy the formula throughout the column.
• Replaced any corrected p value that is greater than 1 by the number 1 by typing the following formula into the first cell below the second dGLN3_Bonferroni_p-value header: <=IF(AL2>1,1,AL2)>, where "dGLN3_Bonferroni_p-value" refers to the cell in which the first Bonferroni p value computation was made. Use the Step (10) strategy to copy the formula throughout the column.

Calculate the Benjamini & Hochberg p value Correction

• Created a new worksheet named "dGLN3_ANOVA_B-H".
• Copied and pasted the "MasterIndex", "ID", and "Standard Name" columns from the previous worksheet into the first two columns of the new worksheet.
• For the following, I used Paste special > Paste values. Copied the unadjusted p values from my ANOVA worksheet and pasted it into Column D.
• Selected all of columns A, B, C, and D and sorted by ascending values on Column D. Clicked the sort button from A to Z on the toolbar, and in the window that appeared, sorted by column D, from smallest to largest.
• Typed the header "Rank" in cell E1. Created a series of numbers in ascending order from 1 to 6189 in this column.
  • This is the p value rank, smallest to largest.
• Typed "1" into cell E2 and "2" into cell E3. Then I selected both cells E2 and E3 and double-clicked on the plus sign on the lower right-hand corner of the selection to fill the column with a series of numbers from 1 to 6189.
• Typed dGLN3_B-H_p-value in cell F1. Then typed the following formula in cell F2: =(D2*6189)/E2 and pressed enter. Proceeded to copy that equation to the entire column.
• Typed "dGLN3_B-H_p-value" into cell G1.
• Typed the following formula into cell G2: =IF(F2>1,1,F2) and pressed enter. Copied that equation to the entire column.
• Selected columns A through G and sorted them by the MasterIndex in Column A in ascending order.
• Copied column G and used Paste special > Paste values to paste it into the next column on the right of my dGLN3_ANOVA sheet.
• Uploaded the .xlsx file that I created to Box. Sent Dr. Dahlquist and Dr. Fitzpatrick an e-mail with the link to the file (e-mail kdahlquist or bfitzpatrick at lmu dot edu).

Sanity Check: Number of genes significantly changed

• performed a more extensive sanity check to make sure that I performed the data analysis correctly. Determined the number of genes that are significantly changed at various p value cut-offs.
• Went to dGLN3_ANOVA worksheet.
• Selected row 1 (the row with your column headers) and selected the menu item Data > Filter > Autofilter (The funnel icon on the Data tab). Little drop-down arrows appeared at the top of each column. This enabled me to filter the data according to criteria we set.

Click on the drop-down arrow for the unadjusted p value. Set a criterion that filtered the data so that the p value has to be less than 0.05.

How many genes have p < 0.05? and what is the percentage (out of 6189)?

How many genes have p < 0.01? and what is the percentage (out of 6189)?

How many genes have p < 0.001? and what is the percentage (out of 6189)?

How many genes have p < 0.0001? and what is the percentage (out of 6189)?

• filtered data to determine the following:

How many genes are p < 0.05 for the Bonferroni-corrected p value? and what is the percentage (out of 6189)?

How many genes are p < 0.05 for the Benjamini and Hochberg-corrected p value? and what is the percentage (out of 6189)?

• Find NSR1 in the dataset and answered the following question:

What is its unadjusted, Bonferroni-corrected, and B-H-corrected p values? What is its average Log fold change at each of the timepoints in the experiment? Note that the average Log fold change is what we called "dGLN3_AvgLogFC_(TIME)" in step 3 of the ANOVA analysis.

• (will compare the numbers I got to the wild type strain and the other strains studied, organized as a table. Used the sample PowerPoint slide to see how my table should be formatted. (Note that you will need to unzip the file after downloading.)

Clustering and GO Term Enrichment with stem (part 2)

Prepared my microarray data file for loading into STEM

• Inserted a new worksheet into my Excel workbook, and named it "dGLN3_stem".
• Selected all of the data from my "dGLN3_ANOVA" worksheet and Pasted special > pasted values into my "dGLN3_stem" worksheet.
• My leftmost column should had the column header "Master_Index". Renamed this column to "SPOT". Column B was named "ID". Renamed this column to "Gene Symbol". Deleted the column named "Standard_Name".
• Filtered the data on the B-H corrected p value to be > 0.05 (that's greater than in this case).
• Once the data was filtered, selected all of the rows (except for your header row) and deleted the rows by right-clicking and choosing "Delete Row" from the context menu. Undid the filter. This ensures that we will cluster only the genes with a "significant" change in expression and not the noise.
• Deleted all of the data columns EXCEPT for the Average Log Fold change columns for each timepoint (for example, dGLN3_AvgLogFC_t15, etc.).
• Renamed the data columns with just the time and units (for example, 15m, 30m, etc.).
• Saved my work. Then used Save As to save this spreadsheet as Text (Tab-delimited) (*.txt). Clicked OK to the warnings and closed my file.
• Note that you should turn on the file extensions if you have not already done so.

Downloading and Extracting the STEM software

I received the files "gene_ontology.obo" and "gene_association.sgd.gz" from Dr.Fitzpatrick and Dr. Dahlquist necessary for STEM.

• Clicked here to go to the STEM web site.
• Clicked on the download link and downloaded the stem.zip file to your Desktop.
• Unzipped the file.
  • This created a folder called stem.
• Downloaded the Gene Ontology and yeast GO annotations and placed them in this folder.
• Clicked here to download the file "gene_ontology.obo".
• Clicked here to download the file "gene_association.sgd.gz".
• Inside the folder, double-clicked on the stem.jar to launch the STEM program.

Running STEM

• In section 1 (Expression Data Info) of the the main STEM interface window, clicked on the Browse... button to navigate to and select my file.
• Clicked on the radio button No normalization/add 0.
• Checked the box next to Spot IDs included in the data file.
• In section 2 (Gene Info) of the main STEM interface window, left the default selection for the three drop-down menu selections for Gene Annotation Source, *Cross Reference Source, and Gene Location Source as "User provided".
• Clicked the "Browse..." button to the right of the "Gene Annotation File" item. Browsed to my "stem" folder and select the file "gene_association.sgd.gz" and clicked Open.
• In section 3 (Options) of the main STEM interface window, made sure that the Clustering Method says "STEM Clustering Method" and did not change the defaults for *Maximum Number of Model Profiles or Maximum Unit Change in Model Profiles between Time Points.
• In section 4 (Execute) clicked on the yellow Execute button to run STEM.

Viewing and Saving STEM Results

• A new window opened called "All STEM Profiles (1)". Each box corresponded 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.
• Clicked 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:", Clicked on the radio button that says "Based on real time". Then closed the Interface Options window.
• 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 pasted it into my PowerPoint presentation.
• Clicked on each of the SIGNIFICANT profiles (the colored ones) that opened a window showing a more detailed plot containing all of the genes in that profile.
• Took a screenshot of each of the individual profile windows and saved the images in my PowerPoint presentation.
• At the bottom of each profile window, there were two yellow buttons "Profile Gene Table" and "Profile GO Table". For each of the profiles, clicked on the "Profile Gene Table" button to see the list of genes belonging to the profile. In the window that appeared, clicked on the "Save Table" button and saved the file to my desktop. Made my filename descriptive of the contents, e.g. "dGLN3_profile22_genelist.txt",
• Uploaded these files to OpenWetWare and linked 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, clicked on the "Profile GO Table" to see the list of Gene Ontology terms belonging to the profile. In the window that appeared, clicked on the "Save Table" button and saved the file to my desktop. Make your filename descriptive of the contents, e.g. "wt_profile#_GOlist.txt", where you use "wt", "dGLN3", etc. to indicate the dataset and where you replace the number symbol with the actual profile number.
• Uploaded these files to OpenWetWare and linked 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).

Analyzing and Interpreting STEM Results

• Selected one of the profiles I saved in the previous step for further interpretation of the data.
  • Why did you select this profile? In other words, why was it interesting 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?
• 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.
• 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.
  • 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?
• Selected 6 Gene Ontology terms from my filtered list (either p < 0.05 or corrected p < 0.05).
  • Making sure to choose terms that are the most significant, but that are also not too redundant. For example, "RNA metabolism" and "RNA biosynthesis" are redundant with each other because they mean almost the same thing.
• Note whether the same GO terms are showing up in multiple clusters.
• Looked up the definitions for each of the terms at http://geneontology.org. In my research presentation, I will discuss 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? Also, what does this have to do with the transcription factor being deleted (for the Δgln3 and Δswi4 groups)?
• Looked up the definitions, at http://geneontology.org.
• Copied and pasteed the GO ID (e.g. GO:0044848) into the search field on the left of the page.
• In the results page, clicked on the button that says "Link to detailed information about <term>, in this case "biological phase"".
  • The definition was on the next results page

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

• In the previous analysis using STEM, I 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. I explored this using the YEASTRACT database.
• Opened the gene list in Excel for one of the significant profiles from my stem analysis. Chose a cluster (profile #22) with a clear cold shock/recovery up/down or down/up pattern.
• Copied the list of gene IDs onto my clipboard.
• Launched a web browser and went to the YEASTRACT database.
• On the left panel of the window, clicked on the link to Rank by TF.
• Pasted my list of genes from my 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 not apply a filter for "Filter Documented Regulations by environmental condition".
• Ranked genes by TF using: The % of genes in the list and in YEASTRACT regulated by each TF.
• Clicked the Search button.
• Answered the following questions:
• In the results window that appeared, 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"?
• Copied the table of results from the web page and pasted it into a new Excel workbook to preserve the results.
• Uploaded the Excel file to OWW and linked to it in your electronic lab notebook.
  • Are GLN3, HAP4, and/or ZAP1 on the list? If so, what is their "% in user set", "% in YEASTRACT", and "p value".
• Selected from this list of "significant" transcription factors, which ones I will use to run the model. I added GLN3, HAP4, and ZAP1 to my list.
• Went back to the YEASTRACT database and followed the link to Generate Regulation Matrix.
• Copied and pasted the list of transcription factors that I identified (plus HAP4, GLN3, and ZAP1) into both the "Transcription factors" field and the "Target ORF/Genes" field.
• Used the "Regulations Filter" options of "Documented", "Only DNA binding evidence"
• Clicked the "Generate" button.
• In the results window that appeared, clicked on the link to the "Regulation matrix (Semicolon Separated Values (CSV) file)" that appeared and saved it to my Desktop. Renamed this file with a meaningful name so that I could distinguish it from the other files I generated.

Visualizing Your Gene Regulatory Networks with GRNsight

• To analyze the regulatory matrix files that I generated above in Microsoft Excel I will visualize them using GRNsight to determine which one will be appropriate to pursue further in the modeling.
• First I needed to properly format the output files from YEASTRACT.
• 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, 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.
• Saved this file in Microsoft Excel workbook format (.xlsx).
• For this adjacency matrix to be used in GRNmap (the modeling software) and GRNsight (the visualization software), I needed to transpose the matrix. Inserted a new worksheet into my Excel file and named it "network". Went back to the previous sheet and selected the entire matrix and copied it. Went to new worksheet and clicked on the A1 cell in the upper left. Selected "Paste special" from the "Home" tab. In the window that appeared, checked 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 needed 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, I copied and pasted the text "rows genes affected/cols genes controlling".
• Finally, for ease of working with the adjacency matrix in Excel, I alphabatized the gene labels both across the top and side.
• Selected the area of the entire adjacency matrix.
• Clicked the Data tab and click the custom sort button.
• Sorted Column A alphabetically, being sure to exclude the header row.
• Then sorted row 1 from left to right, excluding cell A1. In the Custom Sort window, clicked on the options button and select sort left to right, excluding column 1.
• Name the worksheet containing your organized adjacency matrix "network" and Saved.
• Next I visualized what these gene regulatory networks look like with the GRNsight software.
• Went to the GRNsight home page.
• Selected the menu item File > Open and selected the regulation matrix .xlsx file that has the "network" worksheet in it that I formatted above. If the file has been formatted properly, GRNsight should automatically create a graph of your network. I clicked and dragged the nodes (genes) around until I got a layout that I liked and took a screenshot of the results. Pasted this image into my PowerPoint presentation.
• I had nodes (genes) floating around in the display that are not connected to any other nodes, so I deleted (RGM1, HOT1, BAS1, and ZAP1) from the network for the modeling to work properly. Went back to the Excel workbook and network sheet and deleted both the rows and columns with the floating gene's name. Then re-uploaded the edited file to GRNsight to visualize it. I will use this final version in my PowerPoint and subsequent modeling.

Statistical Analysis Results

Protocol Questions

How many genes have p < 0.05? and what is the percentage (out of 6189)?

2135 genes, 34.5%

How many genes have p < 0.01? and what is the percentage (out of 6189)?

1204 genes, 19.5%

How many genes have p < 0.001? and what is the percentage (out of 6189)?

514 genes, 8.31%

How many genes have p < 0.0001? and what is the percentage (out of 6189)?

180 genes, 2.91%

How many genes are p < 0.05 for the Bonferroni-corrected p value? and what is the percentage (out of 6189)?

45 genes, 0.727%

How many genes are p < 0.05 for the Benjamini and Hochberg-corrected p value? and what is the percentage (out of 6189)?

1185 genes, 19.15%

NSR1

p-value unadjusted: 0.00051

p-value Bonferroni-corrected: 3.1363

p-value B-H-corrected: 0.00816

average Log fold change t15: 3.50

average Log fold change t30: 4.53

average Log fold change t60: 2.76

average Log fold change t90: -1.85

average Log fold change t120: -1.87

BIOL_S19_microarray-data_dGLN3_AD

dGLN3_PPT_AD

Stem and GRNsight Results

Media:DGLN3_profiles_genelists_AD.zip

Media:DGLN3_profiles_GOlists_AD.zip

Media:dGLN3_Transcriptionfactors(1)_AD.xlsx.zip

Why did you select this profile? In other words, why was it interesting to you?

I selected profile 22 because between 0 and 60 minutes the genes show little up or down-regulation. However, the genes show a peak of up-regulation at 90 minutes and decreases back towards zero around 120 minutes.

How many genes belong to this profile?

81 genes

How many genes were expected to belong to this profile?

21.2 genes expected

What is the p value for the enrichment of genes in this profile?

8.2E-24

How many GO terms are associated with this profile at p < 0.05?

57

How many GO terms are associated with this profile with a corrected p value < 0.05?

5

Biological Interpretation of GO Terms

GO terms are associated with this profile with a corrected p value < 0.05

GO:0031410, GO:0097708, GO:0031982 (Cytoplasmic Vesicle, Intracellular Vesicle, Vesicle): Any small, fluid-filled, spherical organelle enclosed by membrane

GO:0006793 & GO:0006796 (Phosphorous Metabolic Process): The chemical reactions and pathways involving the nonmetallic element phosphorus or compounds that contain phosphorus, usually in the form of a phosphate group (PO4).

GO terms are associated with this profile with a p value < 0.05

GO:1901564 (organonitrogen compound metabolic process): The chemical reactions and pathways involving organonitrogen compound.

GO:0098796(membrane protein complex): Any protein complex that is part of a membrane.

GO:0042886 (amide transport): The directed movement of an amide, any compound containing one, two, or three acyl groups attached to a nitrogen atom, into, out of or within a cell, or between cells, by means of some agent such as a transporter or pore.

GO:0009628 (response to abiotic stimulus): Any process that results in a change in state or activity of a cell or an organism

How many transcription factors are green or "significant"?

32

Are GLN3, HAP4, and/or ZAP1 on the list? If so, what is their "% in user set", "% in YEASTRACT", and "p value".

• GLN3
  • % in user set: 21.25%
  • % in YEASTRACT: 1.43%
  • p-value: 0.106530713890906

• ZAP1
  • % in user set: 46.25%
  • % in YEASTRACT: 2.40%
  • p-value: 0.000000208128741

• HAP4
  • % in user set: 23.75%
  • % in YEASTRACT: 1.94%
  • p-value: 0.005943792899201

Transcription Factors Chosen:

1. MIG1
2. SKO1
3. PHO2
4. GLN3
5. HAP4
6. MGA2
7. SUT1
8. YHP1
9. CRZ1
10. GIS1
11. ADR1
12. MSN4
13. RAP1
14. RGM1
15. HOT1
16. ZAP1
17. BAS1

• I chose these transcription factors because they had the most significant p-values and because they were factors that my homework partner was not already exploring. These factors were also interesting in terms of their related biological processes.
• NOTE: I removed RGM1, HOT1, ZAP1, and BAS1 because these nodes were not connected to any other transcription factors.

dGLN3_PPT_AD

Conclusion

After processing data for the dGLN3 treatment, unadjusted p-values were found for the entire genome. When using a p-value cut off of 95%, 2136 out of 6189 genes were found to be significant (34.5%). When I filtered for only p-values less than 0.01, 1205 genes were found to display significant fold change. As this cut off was further decreased to 0.001 and 0.0001 the percentage of significant gene transcriptions were 8.32% and 2.92% respectively. After applying adjustments to the p-value through Bonferroni p-value Correction and Benjamini & Hochberg p-value Correction, the number of significant changes in gene transcription at p<0.05 dropped to 19.15% for Benjamini & Hochberg p-value correction and all the way to 0.7% for Bonferroni p-value correction. Focusing in on the gene expression of NSR1 specfically, it was discovered that the unadjusted p value was 0.00051. The Bonferroni p-value correction gave 3.1363 and the Benjamini & Hochberg p-value correction produced a p-value of 0.00816. After averaging the change in gene expression from the control treatment to the experimental condition it is apparent that there is a change in gene expression between 60-90 minutes indicated by the fold change of 2.759 to -1.85. The purpose of this endeavor was accomplished and the number of significant fold changes in genes were calculated by assessing unadjusted and corrected p-values.

STEM clustering producing Profile 22 displayed little up or down-regulation between 0-60 minutes. Up-regulation is then found at 90 minutes, eventually sloping back towards zero at 120 minutes. The behavior of the 81 genes in this profile lead to the question of why these genes share a similar expression pattern and what exactly is contributing to the up-regulation of these genes at 90 minutes of cold shock treatment. There were 5 GO terms associated with this profile with a corrected p value < 0.05. Three of the five GO values related to cell vesicles, small organelles used for transport. A potential explanation for this phenomenon could be the response to the rigidity of the cell membrane caused by the cold temperature. As a result, the expression of vesicles was increased to help carry products to help change the number of unsaturated phospholipids in the cell membrane to maintain fluidity. The other two GO values pertained to phosphorous metabolic process. Metabolic rates may be reduced by the cold temperature because enzymes involved with glycolysis can not function at a temperature that is considerably lower than their optimal temperature. Phosphorous metabolic process could be related to production of ATP at low temperatures. GRNsight aided in discovering the transcription factors related to the changes in gene expression as well as the relationship between the transcription factors. All of the transcription factors listed within the results section had multiple connections in the network besides PHO2 and MGA2. The transcription factors HAP4, RAP1, CRZ1, and ADR1 had many connections.It is logical that many transcription factors would be linked to HAP4 because it is involved with respiration, an essential function for yeast to undergo at cold temperatures. RAP1 is involved in regulation of telomere structure. Low temperature has been found to cause telomere entanglements which can cause damage to DNA (Paeschke et al., 2010). CRZ1 has a link to calcium ion homeostasis. Controlling calcium ion concentration depsite fluctuating environmental conditions displays the cell's ability to perform negative feedback under cold temperature stimuli. ADR1 is a transcription factor necessary for activation of glucose-repressible alcohol dehydrogenase. This enzyme catalyzes the conversion of ethanol to acetaldehyde. Since there is a possibility for aerobic respiration to be disturbed by cold shock, yeast may resort to anaerobic respiration. Using STEM to find clusters and GRNsight to find relationships between transcription factors, accomplished the goal of determining the connection between up-regulation of the genes in profile 22 at 90 minutes and biological explanations. However, one aspect of profile 22 that is still not entirely known is the reasoning for why gene expression returns to a steady state of no up or down-regulation at 120 minutes.

Acknowledgements

• Had several text conversations with my homework partner Leanne Kuwahara to compare statistical test ouputs and clarify any discrepancies. We also discussed which transcription factors we chose so there was no overlap.
• I received in class help from Dr. Dahlquist to upload excel documents to box.
• All necessary files provided by Dr.Fitzpatrick and Dr. Dahlquist. The specific files are listed within the methods section.

Except for what is noted above, this individual journal entry was completed by me and not copied from another source.

Austindias (talk) 16:32, 20 February 2019 (PST)

References

Dahlquist, K. and Fitzpatrick, B. (2019). BIOL388/S19:Week 4. [online] openwetware.org. Available at:Week 4 Assignment Page [Accessed 13 Feb. 2019].

Dahlquist, K. and Fitzpatrick, B. (2019) https://lmu.app.box.com/file/396633824966 [Accessed 13 Feb. 2019].

EMBL-EBI, I. (n.d.). InterPro. Retrieved from https://www.ebi.ac.uk/interpro/entry/IPR018287

European Bioinformatics InstituteProtein Information ResourceSIB Swiss Institute of Bioinformatics. DNA-binding protein RAP1. Retrieved from https://www.uniprot.org/uniprot/P11938

Gene Ontology Resource. (n.d.). Retrieved from http://geneontology.org/

GRNsight. (n.d.). Retrieved from http://dondi.github.io/GRNsight/

Paeschke, K., McDonald, K. R., & Zakian, V. A. (2010). Telomeres: structures in need of unwinding. FEBS letters, 584(17), 3760-3772.

Short Time-series Expression Miner (STEM). (n.d.). Retrieved from http://www.cs.cmu.edu/~jernst/stem/

YEASTRACT. (n.d.). Retrieved from http://yeastract.com/

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