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Visualize KEGG Pathways

We'll start from the m3.xlarge Amazon machine booted & configured in salmon.rst. If you are just running this, you'll need to run the apt-get commands, install khmer, and mount the data snapshot before continuing.

First, create a working directory and download the swiss-prot to kegg orthology database linkage file:

cd
mkdir kegg
cd kegg
curl -O https://raw.githubusercontent.com/ngs-docs/2015-nov-adv-rna/master/swiss_orthology.list

Note: this file was downloaded on 11/02/2015, but this database is updated regularly. In order to download a recent version, go to http://www.genome.jp/linkdb/ and click on "SWISS-PROT", then "Orthology," then click the "Download" button just under the database linkage map.

Getting Kegg Orthology Numbers

We need the blastx results from diamond_blast.rst, which you can download with:

curl -O https://s3-us-west-1.amazonaws.com/dib-training.ucdavis.edu/mRNAseq-non-2015-05-04/nema.x.swissprot.diamond.m8.gz
gunzip nema.x.swissprot.diamond.m8.gz

Download and run a python script to relate your blastx results to their KEGG Orthology numbers using the linkage database information:

curl -O https://raw.githubusercontent.com/ngs-docs/2015-nov-adv-rna/master/getKOfromBlastx.py

python getKOfromBlastx.py --blastx nema.x.swissprot.diamond.m8 --spToKO swiss_orthology.list

See the results of the file with:

less nema.x.swissprot.diamond_keggOrthology.txt

Type 'q' to exit out of the file viewer.

Visualize Kegg Pathways Present in the Data

Install some extra python modules to visualize KEGG maps:

sudo pip install reportlab
git clone https://github.com/bluegenes/KGML.git

Run the python script to visualize the KEGG annotations. ko01100 is the KEGG Metabolic Pathways overview map:

python ./KGML/simpleDrawKeggMap.py --path ko01100 --blastKO nema.x.swissprot.diamond_keggOrthology.txt

Copy the pdf file to your computer so you can open it with a pdf viewer. The pathways that were found in the nema data are displayed as thickened dark gray lines:

# This code uses the same information we used to login:
# KEY = your private key
# YOUR-AMAZON-MACHINE = the "Public DNS" of your amazon machine
scp -i KEY.pem ubuntu@YOUR-AMAZON-MACHINE:~/kegg/ko01100.pdf ~/Desktop

This script can be used to visualize any KEGG pathway. For example, TCA cycle:

#run script
python ./KGML/simpleDrawKeggMap.py --path ko00020 --blastKO nema.x.swissprot.diamond_keggOrthology.txt
#download pdf file(s)
scp -i your-key.pem [email protected]:~/kegg/*.pdf ~/Desktop

In this map, orthologs are represented as boxes, rather than lines (as in the metabolic map, above). Again, orthologs that are colored dark gray are present in your data.