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When working with transcriptomic data sets, the first step that most researchers will typically conduct is differential gene expression analysis. Although this is an essential first step, it is limited in the sense that it will only inform researchers which genes are up or down-regulated, relative to a control condition.
In some cases, it may be more intuitive to visualize differential expression analysis results in a more realistic context. Pathway maps are collections of enzymatic reactions, metabolites, and intermediates which have been compiled into a “map” to depict general cellular and metabolic processes.
There are many different repositories that
have compiled such maps to be used with a broad array of different organisms, some of the most popular being the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Escher Visualizations.
In this assignment, we will demonstrate pathway visualizations of KEGG maps with differential expression data sets using a web tool called pathView – it also has an R-compatible package; however, we will use the web tool for simplicity, as this is not a computer science course.
The results from differential gene expression analysis can be used as input for path view and the user selects the respective pathway(s) which they would be interested in when conducting their own research. pathView supports visualization of gene expression data (the gene identifiers need to be compatible with the organism being studied) as well as metabolite data:
The color-coated legends depict enzymatic or metabolite changes on the Log2FoldChange scale, relative to a control condition; enzymatic reactions highlighted in red imply more activity, relative to the control, while enzymatic reactions highlighted in green symbolize a reduction in activity, relative to a control.
For this assignment, please navigate to the pathView user home (link below) and select one of the examples (there are 4 of them). Once you have clicked on one of the examples, choose 3 pathways (not including propanoate metabolism) and run the analysis using the demo data sets which have already been pre-loaded. Take a screenshot of the three pathway visualizations and compile them into a word document or PDF file to submit for assignment 2.
EditEditEditEditFor your final notebook check, please submit a Word or PDF document with some notes describing the advantages that pathway visualization has over standard differential gene expression analysis. How might a researcher use pathway visualizations after conducting differential gene expression analysis to further their research?
For your final notebook check, please submit a Word or PDF document with some notes describing the advantages that pathway visualization has over standard differential gene expression analysis. How might a researcher use pathway visualizations after conducting differential gene expression analysis to further their research?
For your final assignment of Online Part 3, you will design your own hypothetical experiment and write a short prompt to describe how you would go about conducting such an experiment from start to finish, including how you would grow cells, how to prepare them for RNA-seq, and what you would do after you have obtained raw gene counts. Get creative with this assignment – its your chance to think like a scientist in a real-world scenario.
If you need a general idea of how a prompt should look, I have attached a short sample prompt:
Please submit a minimum 300 word PDF or Word document depicting your experiment by Sunday 5/2 at 11:59 PST.