Genome Spot

I recently needed to convert MSigDB gene sets to mouse so I thought I would share.

GO.v5.2.symbols_mouse.gmt
kegg.v5.2.symbols_mouse.gmt
msigdb.v5.2.symbols_mouse.gmt
reactome.v5.2.symbols_mouse.gmt

Below is the code used to do the conversion. It requires an input GMT file of human gene symbols as well as a human-mouse orthology file. You can download the ortholog file here. As the name suggests, it is based on data downloaded from Ensembl Biomart version 87.

Running the program converts all human gmt files. It requres gnu parallel which can be easily installed on Ubuntu with "sudo apt-get install parallel"


#!/bin/bash

conv(){
line=$1
  NAME_DESC=`echo $line | cut -d ' ' -f-2`

  GENES=`echo $line | cut -d ' ' -f3- \
  | tr ' ' '\n' | sort -uk 1b,1 \
  | join -1 1 -2 1 - \
  <(cut -f3,5 mouse2hum_biomart_ens87.txt \
  | sed 1d | awk '$1!="" && $2!=""' \
  | sort -uk 1b,1) | cut -d ' ' -f2 \
  | sort -u…

Gene sets are distilled information about molecular profiling experiments and can generated based on other features shared by groups of genes such as chromosomal position, sequence, co-regulation, functional information, etc.

These are a valuable resource because they suggest similarities between different molecular profiling experiments or phenomona and lead researchers into understanding the factors that drive the trends in profiling experiments such as gene expression assays by microarray or RNA-seq.

To truly grasp the importance of quality gene sets, consider that the original paper describing the GSEA algorithm has accumulated 3144 citations since 2003, while the paper describing the software and wider applicability of GSEA has 7166 citations. The latter paper has also attracted positive comments from experts in the field on PubMed, here is one that I couldn't agree with more. In the words of Rafael Irizarry, "The idea of analyzing differential expression for groups of g…