goatools

Python scripts to find enrichment of GO terms

Python ⭐ 0

README

Tools for Gene Ontology

:Author: Haibao Tang (tanghaibao), Brent Pedersen (brentp), Aurelien Naldi (aurelien-naldi)
:Email: [email protected]
:License: BSD

.. contents ::

Description

This package contains a Python library to

process over- and under-representation of certain GO terms, based on Fisher's exact test. Also implemented several multiple correction routines (including Bonferroni, Sidak, and false discovery rate).
process the obo-formatted file from Gene Ontology website <http://geneontology.org>_. The data structure is a directed acyclic graph (DAG) that allows easy traversal from leaf to root.

Installation

Python version >= 2.6, try install this package first. Within this folder::

easy_install .
.obo file for the most current gene ontology <http://www.geneontology.org/>_::

wget http://geneontology.org/ontology/obo_format_1_2/gene_ontology.1_2.obo
fisher <http://pypi.python.org/pypi/fisher/>_ module for calculating Fisher's exact test::

easy_install fisher

And put it in the current folder where you want to do your analysis.

If you need to plot the ontology lineage, you need the following to tools to be installed.

Graphviz <http://www.graphviz.org/>_, for graph visualization.
pygraphviz <http://networkx.lanl.gov/pygraphviz/>_, Python binding for communicating with Graphviz::

easy_install pygraphviz

Cookbook

run.sh contains example cases, which calls the utility scripts in the scripts folder.

Find GO enrichment of genes under study
::::::::::::::::::::::::::::::::::::::::::
see find_enrichment.py for usage. It takes as arguments files containing:

gene names in a study
gene names in population (or other study if --compare is specified)
an association file that maps a gene name to a GO category.

please look at data/ folder to see examples on how to make these files. when ready, the command looks like::

python scripts/find_enrichment.py --pval=0.05 --indent data/study data/population data/association

and can filter on the significance of (e)nrichment or (p)urification.
it can report various multiple testing corrected p-values as well as
the false discovery rate.

The "e" in the "Enrichment" column means "enriched" - the concentration of GO term in
the study group is significantly higher than those in the population. The "p" stands
for "purified" - significantly lower concentration of the GO term in the study group
than in the population.

Read and plot GO lineage
::::::::::::::::::::::::::::::::::::
see plot_go_term.py for usage.

plot_go_term.py can plot the lineage of a certain GO term, by::

python scripts/plot_go_term.py --term=GO:0008135

this will plot the following image.

.. image:: http://lh6.ggpht.com/_srvRoIok9Xs/S9HhleQrk5I/AAAAAAAAA5U/dzVIvjlYCQU/s800/GO_0008135.png
:alt: GO term lineage

Sometimes people like to stylize the graph themselves, use option --gml to
generate a GML output which can then be used in an external graph editing
software like Cytoscape <http://www.cytoscape.org/>. The following image is
produced by importing the GML file into Cytoscape using yFile orthogonal
layout and solid VizMapping. Note that the GML reader plugin <https://code.google.com/p/graphmlreader/> may need to be
downloaded and installed in the plugins folder of Cytoscape::

python scripts/plot_go_term.py --term=GO:0008135 --gml

.. image:: http://tinyurl.com/by2m57n
:alt: GO term lineage (Cytoscape)