Genepage-insight

Overview

The instructions below provide users with a copy of this program which can be directly used or adapted for their own project. The program require 2 plain text files, one containing a list of geneIDs and the other with a list of keywords describing the phenotypes the user suspects the genes have influence over. The script can work for either rice, wheat or arabidopsis.

The tabular output would provide the users with the chromosome and start position for every gene, a knetscore which assesses the relevance of each gene to the specified phenotype or phenotypes and a Knetminer genepage url per gene. The url leads the user into Knetminer's network view which accelerates research by outlining orthology of the gene as well as traits and publications related to it.

Prerequisites

• The script can run on any computer with access to Python2.7 and above or any versions of Python3.

• The program does not require heavy computational resources. However, as the user may prefer high performance computing the instructions on how to set up and run the program on a node managed by the Easybuild framework has been included below. The User should read set up instructions specific to any other HPC frameworks.

• Python virtual environments, e.g. virtualenv for python2 or pyvenv for python3. If the user does not have root permission on Easybuild a virtual environment is required for installation of requests, numpy and pandas through pip. See 4.Installing python request library in Instructions on how to do this.

Tutorial and usage instructions

This is a quick tutorial to get the user started by reproducing the outputs of map_snp_to_gene.py for 2 different GWAS result spreadsheets, GAPIT.MLM.DTF.GWAS.Results.csv and GAPIT.MLM.blupWidth.GWAS.Results.csv as seen in the 2 directories of the same names. The tutorial generally assumes the user is using linux managed by Easybuild.

1.Downloading the repository

Clone this repository with the GitHub URL using either Git or a Git GUI. The user should obtain a directory named gwas-gene-discovery containing identical contents to the GitHub repository.

2. Accessing compute node on Easybuild

The user can check available compute nodes by the command:

sinfo 

If available, login to a standard compute node on Rothhpc4 using:

srun --pty bash -i

3.Setting up a virtual environment on Easybuild

A virtual environment is required for pip installation of numpy, pandas and requests. Check all the available versions of python currently on cluster:

module avail Python

After the user has decided on a version of Python2 that is 2.7 and above or Python3 execute the commands below.

module load <Python version>
virtualenv <name of Python virtual environment>
source </path to env>/bin/activate/

The user can return to the virtual environment in a new session after logging out by:

module load <Python version>
source </path to env>/bin/activate/

4.Installing python request library

Requests is needed for the steps that send HTTP request protocols found in the script while Numpy and Pandas are required for tabulating information from Knetminer API. Pandas library includes Numpy therefore use pip to install the 2 libraries:

pip install requests
pip install pandas

5.Execution of script

The command:

python genepage_insight.py -h

Returns the usage of the script as the following:

usage: genepage-insight.py [-h] file list species

The mandatory arguments are:

• File. A plain text file containinig a list of input genes line by line. An example text file used for tutorial is example_list.txt. Example_list.txt contains a list of the first 5 wheat genes belonging to solstice and skyfall variety from CerealsDB returned by a prior investigation.

• List. A plain text file containing one or more short description (one or two words) of the phenotype or phenotypes the genes of interest might potentially be influencing, listed line by line. An example list, mock_keyword_list.txt can be found in the repository.

• Species. The species of organism subjected to gwas which the script will return gene IDs specific to. The options are currently 3 species represented by an integer value as shown below:

  • 1 represents rice (Japonica variety only)
  • 2 represents wheat
  • 3 represents arabidopsis

If the User has either a standard or Easybuild terminal set up with requests installed, they can run the following command. This will reproduce the directories: ./example_list which contains results.txt summarising the Knetminer findings on genes provided.

python genepage-insight.py example_list.txt mock_keyword_list.txt 2

6. Output information

The output will be a tabular text file containing 5 columns of Genes, Knetscore, chromosome, gene start position and network view. To see an example of the result of the script, see ./example_list/results.txt

The Knetscore column assesses the relevance of the gene to the trait or traits of interest provided in the keyword list file. The higher the knetscore, the more likely the gene is to influence the trait.

The network view column contains URL addresses to networks of Knetminer which displays related orthologues, other traits encoded by the gene and publications etc.

External tools included

Knetminer V3.0

Authors

Keywan-Hassani Pak

Colin Li

Acknowledgement

Bioinformatics Department, Computational and Analytical Sciences, Rothamsted Resarch