TOGA 2016

Introduction

TOGA is a new method that integrates gene annotation, inferring orthologs and classifying genes as intact or lost.

TOGA implements a novel machine learning based paradigm to infer orthologous genes between related species and to accurately distinguish orthologs from paralogs or processed pseudogenes. ¹

TOGA makes use of CESAR 2.0 which is also installed on our cluster and will be automatically loaded when loading TOGA.

Installed version(s)

This version is installed and currently available

on environment hpc-env/8.3:

• TOGA/1.0-intel-2019b-Python-3.7.4

Loading TOGA

To load the desired version of the module, use the command, e.g.

module load hpc-env/8.3
module load TOGA

Always remember: this command is case sensitive!

If you want to find out more about TOGAon the HPC cluster, you can use the command

module spider TOGA

This will show you basic information e.g. a short description and the currently installed version.

Using TOGA

TOGA is callable by executing the script toga.py

Since TOGA comes with some test files, we will make use of them to give you a small example on how to use the program:

## Load TOGA 
ml hpc-env/8.3
ml TOGA

##Create a folder for the tests input and output data and copy them into said directory
mkdir $WORK/TOGA_TEST
cd $WORK/TOGA_TEST
cp -r $EBROOTTOGA/test_input .

## calling TOGA on the files (human and mouse)

toga.py  test_input/align_micro_sample.chain test_input/annot_micro_sample.bed  test_input/hg38.micro_sample.2bit test_input/q2bit_micro_sample.2bit --pn micro_test_out  --kt  --cjn 24 --chn 1 --ms

## At $WORK/TOGA_TEST you should now find the output of this wide-scale test.

Documentation

The full documentation can be found here.