After RNA-seq data analysis, you will get a list of differentially expressed (DE) genes. In this exercise, we will use two different ways to test which function categories are enriched in your DE gene list.
If you work on a non-model organism, chances are that you do not have Gene Ontology (GO) annotation of your reference genome. GO annotation is required for function enrichment analysis. We will use BLAST2GO software to generate GO annotation.
There are three GO domains: cellular component (CC), biological process (BP), and molecular function (MF). In this exercise, we will only do function enrichment test for the BP domain. When you do your real project, you will need to test all three.
The topGO package of Bioconductor will be used to do ORA. We will perform ORA on RNA-seq results of a yeast experiment. The reference is https://www.ncbi.nlm.nih.gov/pubmed/16606683 .
Download GO annotation from Ensembl BioMart.
Ensembl (https://ensembl.org/) is a good resource to retrieve existing GO annotation (For plant genomes, go to https://plants.ensembl.org/ )
Convert the file you downloaded into a format compatible with topGO. Use FileZilla to upload the "mart_export.txt" file to your assigned Linux server, keep it in the /workdir/$USER.
We provide you with a script that converts the BioMart file into topGO format. Run the commands:
After this, you would see a new file created: topgoAnnot.
· topgoAnnot: the GO annotation file in the topGO required format;
· refset: a text file with list of reference set of genes with one gene per line (normally all genes that have none-zero expression in your experiments)
· DElist_pos: a text file with the list of up-regulated DE genes.
· 0.1: cutoff p-value for enriched categories.
· BP: test for biological processing GO. You can also test for MF (molecular function) and CC (cellular component).
· myBP: output file name prefix.
After this, you should find two new files "myBP".
You can use FileZilla to download both files to your laptop to examine the contents.
In the myBP file, the enriched GO ids are listed sorted by p-values in the column "topgoFisher".
In your publication, you can say that "gene set enrichment analysis was performed with BioConductor topGO package, using its default method weight01, and p-value threshold is set at ...".
We provide you with two data files (bp.gmt & genes.rnk) to run GSEA. When you work on your own project, here is how to prepare the two files.
You can either GSEA on your laptop, or on the LINUX server.
Install GSEA. Go to http://software.broadinstitute.org/gsea/index.jsp and click "Downloads" to download and install the software.
(Optional) Install Cytoscape. Go to https://cytoscape.org/ and click "Download x.x.x" to download and install the software. After installation, you will need to install an app within Cytoscape named "EnrichmentMap". Start Cytoscape. Click "App"->"App Manager". Search for "EnrichmentMap" and install this app.
Use FileZilla to download the two data files bp.gmt & genes.rnk from the directory /shared_data/annotation2019_2/;
Click "Load data", then click "Browse for files", and open the two files bp.gmt & genes.rnk
Click "Run GSEA Preranked", and set the following:
Click "Run". It would take a few minutes. After it is done, you should see "Success".
*Enrichment statistics: the high p value would put more weight to genes with high fold change when calculating enrichment scores (ES). Use the default "Weight" in most cases.
On the page, you should see two block of "Enrichment in phenotype". One for gene sets enriched in up-regulated genes (na_pos) and one for gene sets enriched in down-regulated genes (na_neg). Click "Detailed enrichment results in html ", you would see enriched gene sets.
(Optional) Enrichment map visualization. Interpretation of the map can be found at this page: https://enrichmentmap.readthedocs.io/en/docs-2.2/Tutorial_GSEAInterface.html
Run GSEA command on LINUX (optional).
With GSEA open on your laptop, click "command" at the bottom of the GSEA window. Copy-paste the command to a text editor.
You need to change a few things for this command to run on BioHPC computer:
Now run the command on Linux server. After done, you can examine the results in the "out" directory.
In part 1 of this exercise, you have copied all files in /shared_data/annotation2019_2/ to /workdir/$USER. You will run BLAST2GO on the fasta file: annot_exercise_aa.fasta
Run BLAST2GO on cbsumm10.
As we only have one license of BLAST2GO. It is installed on the computer cbsumm10.biohpc.cornell.edu.
Login to the computer cbsumm10.
After this step, you will get three result files: