Sulfiknights DA Week 14

Sulfiknight Links
BIOL Databases Main Page	Sulfiknights: Project Overview Page	Final Project Deliverables Requirements	Sulfiknights: Final Project Deliverables	Members	Project Manager & Quality Assurance: Naomi Tesfaiohannes	Quality Assurance: Joey Nimmers-Minor	Data Analysis: Ivy-Quynh Macaraeg & Marcus Avila	Designer: DeLisa Madere
				Assignment Pages	Week 11	Week 12/13	Week 15

Template:Sulfiknights

Purpose

• To analyze data collected by Thorsen et al. regarding gene expression changes in response to sulfate exposure through using an ANOVA, running STEM, YEASTRACT, GRNsight, GRNmap, and Microsoft Access
• To check the efficacy of the work done by Thorsen et al.

Methods and Results

Loading Data into STEM

1. Downloaded all STEM software needed found on Week 9.
2. Deleted the "1080" data column from the "swtVnwt_1mM_stem" sheet found in the Thorsen Data Excel File.
   • This was done so that the profile graphs were clearer
3. Converted this file into a .txt file
4. Ran STEM with this file under these parameters:
   • No normalization/add 0
   • Spot IDs included in the data file
   • Browse: gene_association.sgd.gz
5. Selected "X-axis scale should be: Based on real time" in the Interface Options on the All STEM Profiles (1) pop-up window.
6. All genelists and GOlists for the significant profiles (9,23,40, and 48) were downloaded and saved as .txt files.
   • Profiles 9 and 23 were colored red to show clusters that were involved in down-regulation.
   • Profiles 40 and 48 were colored green to show clusters that were involved in up-regulation.

Creating regulatory networks with YEASTRACT

1. Copied the gene lists from Profile 9 and 23.
2. Inputted this list into the YEASTRACT database and under the "Rank by TF".
3. Took the first 17 significant transciption factors and put them into both boxes of the Generate Regulation Matrix page.
   • CSV file that was generated was converted into an Excel file.
4. File was delimited and organized so that "p"s were deleted, everything was captalized, and the regulatees and regulators were alphabetized and in the same order.
5. Position A1 was changed to the text: "rows genes affect/cols genes controlling".
6. We deleted transcription factors that were not connected to others and/or only connected to itself.
7. Sheet was named "network", saved, and inputted into the GRNsight home page.
   • Resulting matrices were organized into Grid Layout and saved.

Steps 1-7 were repeated with the genes from Profiles 40 and 48. In the end, we had two networks and two GRNsight matrices (black and white).

Creating GRNmap Input Workbook

1. Downloaded MS Access database created by Designer
2. Imported network for Profiles 9 and 23
3. Created production_rates, degradation_rates, and Thorsen_2007_log2_expression sheets by following steps:
   • Clicked "Create", then "Query Design", and selected "network" and desired sheet
   • Arranged arrow so that id was correlated to Standard name and joined properties
   • Dragged id and desired sheets (production rates, degradation rates, and Thorsen data at each time point, 3 replicates each)
   • Right clicked in grey space, Make Table Query
   • Ran query and saved query
4. Each sheet was copied and pasted into the GRNmap Input Worksheet
5. The original network sheet was pasted into Input Worksheet
6. The Network_weights sheet also contained the original network
7. The optimization_parameters sheet was copied from sample worksheet and mutants were deleted in row 15 to only show "wt"
   • Row 14 only contains time points 15, 30, and 60
8. the threshold_b sheet contained "0"s for each transcription factor
9. Input workbook was saved

Steps 1-9 were repeated with network for Profiles 40 and 48. In the end, we had two input workbooks (one for 9 and 23, and one for 40 and 48).

Running Input Files with MATLAB

1. Opened MATLAB R2014b, opened GRNmodel.m
2. Ran MATLAB with Input Workbook for Profiles 9 and 23
3. Once it was done running, we saved the files (.jpgs, output .xlsx file, and .mat file) and zipped them together
4. We submitted the output .xlsx file into the GRNsight page to visualize results as a colored Grid matrix.

These steps were repeated with the Input Workbook for Profiles 40 and 48. In the end, we have two separate, zipped output files for Profiles 9 and 23 and for Profiles 40 and 48. We also have two colored GRNsight matrices.

Data and Files

Excel Spreadsheet with ANOVA Results/STEM Formatting

Thorsen Data (Updated 12/2/19)

PowerPoint of ANOVA Table

PowerPoint of ANOVA table and Screenshots of STEM Profile Results

Profiles 9 and 23 input network (Excel)

Profile 40 and 48 input network (Excel)

Gene List and GO List files

Zipped Gene Lists for Significant Profiles

Zipped GOLists for Significant Profiles

YEASTRACT "Rank by TF" Results

Profiles 9 and 23

Profiles 40 and 48

GRNmap input workbook

GRNmap Input Worksheet: Profiles 9 and 23

GRNmap Input Worksheet: Profiles 40 and 48

GRNmap output workbook and output plots

GRNmap Output Files: Profiles 9 and 23

GRNmap Output Files: Profiles 40 and 48

MS Access database

MS Access Database of Thorsen & Dahlquist data

Query design for populating a GRNmap input workbook from the database

Profiles 9 and 23:

Profiles 40 and 48:

Merge completed databases into a single database

BIOL_478_Sulfiknights_BioDB_CombinedDatabase.zip ‎

Access_Database_Schema_Screenshot.PNG

Conclusion

The data analysis performed produced various graphs showing expression trends and networks that depicted regulation between genes. After running STEM, four profiles were produced (profiles 9, 23, 40, 48) and profiles 9 and 23 were combined for further analysis because both illustrating gene repression, while profiles 40 and 48 were also combined because they both illustrated gene expression. The network illustrating gene regulation for profiles 9 and 23 suggests RAP1 was highly regulated during exposure to arsenic and the network illustrating gene regulation for profiles 40 and 48 suggest both MSN2 and MSN4 were highly regulated during exposure to arsenic.

References

• GRNsight. Retrieved December 3, 2019, from https://dondi.github.io/GRNsight/ .

• Short Time-series Expression Miner (STEM). Retrieved November 26, 2019, from http://www.cs.cmu.edu/~jernst/stem/ .

• Thorsen, M., Lagniel, G., Kristiansson, E., Junot, C., Nerman, O., Labarre, J., & Tamás, M. J. (2007). Quantitative transcriptome, proteome, and sulfur metabolite profiling of the Saccharomyces cerevisiae response to arsenite. Physiological genomics, 30(1), 35-43. DOI: https://doi.org/10.1152/physiolgenomics.00236.2006

• Week 9. Retrieved November 26, 2019, from https://xmlpipedb.cs.lmu.edu/biodb/fall2019/index.php/Week_9 .

• YEASTRACT. Retrieved December 3, 2019, from http://www.yeastract.com/formregmatrix.php .

Acknowledgements

We would like to thank User:Kdahlquist for helping with this investigation. Except for what is noted above, this individual journal entry was completed by the member of team Sulfiknights and not copied from another source.