Question: Downstream RNA seq analysis
0
gravatar for Payal
11 months ago by
Payal100
Payal100 wrote:

Hi,

What are the most common donwstream analysis steps and tools for RNA seq analysis, after getting the diferentially expressed genes?

I know this is a very generic question and its supposed to be fine tuned to particular research, but in general what are your goto tools and pipelines for a standard RNA seq. I am planning to do a workshop for my institution, so just gathering others opinions than mine! Will really appreciate your suggestions.

Thanks, Payal

rna-seq • 304 views
ADD COMMENTlink modified 11 months ago by dsull1.7k • written 11 months ago by Payal100
2

You can do a lots of things in down stream process such as gene ontology, pathways analysis, protein protein interaction or you can see how many of the genes are belong to which classes of protein and family. The question is very generic please make precise. What really you want.

ADD REPLYlink written 11 months ago by skjobs0123170
1

I think one way to go would be to do a gene set enrichment analysis to look for pathways (or GO terms) where component genes are over-represented. This would help translate a set of genes to have some sort of biological meaning.

ADD REPLYlink written 11 months ago by _r_am32k
1
gravatar for brianj.park
11 months ago by
brianj.park50
Montréal, Canada
brianj.park50 wrote:

You could take the list of differentially expressed genes and their p-values to do some gene ontology enrichment analysis. TopGO is a useful tool on R for that.

ADD COMMENTlink written 11 months ago by brianj.park50
1
gravatar for dsull
11 months ago by
dsull1.7k
UCLA
dsull1.7k wrote:

1) Try EnrichR: https://amp.pharm.mssm.edu/Enrichr/

Lots of features -- pathways, gene ontology, etc. Simply copy and paste the list of differentially expressed genes into the text box. Would recommend running it on the upregulated and downregulated genes separately, but this depends on your experiment.

2) Try String-DB: https://string-db.org/cgi/input.pl?input_page_active_form=multiple_identifiers

Can create cool protein-protein interaction network graphs.

3) Also, GSEA is a great tool to use.

4) There's WGCNA if you have a large number of samples and are interested in correlation and co-expression (rather than differential gene expression).

5) Finally, at your workshop, teach some ways to visualize the results: PCA or MDS plots, heatmaps, volcano plots, MA plots, etc.

ADD COMMENTlink written 11 months ago by dsull1.7k
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