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Python scripts to find enrichment of GO terms

Project Description

Latest PyPI version Number of PyPI downloads License
Author:Haibao Tang (tanghaibao), Brent Pedersen (brentp), Aurelien Naldi (aurelien-naldi), Patrick Flick (r4d2), Jeff Yunes (yunesj)


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. The data structure is a directed acyclic graph (DAG) that allows easy traversal from leaf to root.
  • map GO terms (or protein products with multiple associations to GO terms) to GOslim terms (analog to the script supplied by


  • Make sure your Python version >= 2.6, install it via PyPI:

    easy_install goatools
  • .obo file for the most current gene ontology:

  • .obo file for the most current GO Slim terms (.e.g generic GOslim)



  • fisher module for calculating Fisher’s exact test:

    easy_install fisher

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

  • Graphviz, for graph visualization.

  • pygraphviz, Python binding for communicating with Graphviz:

    easy_install pygraphviz

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

Find GO enrichment of genes under study

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

python scripts/ --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 for usage. can plot the lineage of a certain GO term, by:

python scripts/ --term=GO:0008135

This command will plot the following image.

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. 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 may need to be downloaded and installed in the plugins folder of Cytoscape:

python scripts/ --term=GO:0008135 --gml
GO term lineage (Cytoscape)

Map GO terms to GOslim terms

See for usage. As arguments it takes the gene ontology files:

  • the current gene ontology file gene_ontology.1_2.obo
  • the GOslim file to be used (e.g. goslim_generic.obo or any other GOslim file)

The script either maps one GO term to it’s GOslim terms, or protein products with multiple associations to all it’s GOslim terms.

To determine the GOslim terms for a single GO term, you can use the following command:

python scripts/ --term=GO:0008135 gene_ontology.1_2.obo goslim_generic.obo

To determine the GOslim terms for protein products with multiple associations:

python scripts/ --association_file=data/association gene_ontology.1_2.obo goslim_generic.obo

Where the association file has the same format as used for

The implemented algorithm is described in more detail at the go-perl documenation of map2slim.

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