Tools for Gene Ontology
|Author:||Haibao Tang (tanghaibao), Brent Pedersen (brentp), Fidel Ramirez (fidelram), Aurelien Naldi (aurelien-naldi), Patrick Flick (patflick), Jeff Yunes (yunesj), Kenta Sato (bicycle1885), Chris Mungall (cmungall), Greg Stupp (stuppie), DV Klopfenstein(dvklopfenstein)|
- iPython Notebooks
- Run a Gene Ontology Enrichment Analysis (GOEA)
- Show many study genes are associated with RNA, translation, mitochondria, and ribosomal
- Report level and depth counts of a set of GO terms
- Find all human protein-coding genes associated with cell cycle
- Calculate annotation coverage of GO terms on various species
- Determine the semantic similarities between GO terms
This package contains a Python library to
- process over- and under-representation of certain GO terms, based on Fisher's exact test. With numerous multiple correction routines including locally implemented routines for Bonferroni, Sidak, Holm, and false discovery rate. Also included are multiple test corrections from statsmodels: FDR Benjamini/Hochberg, FDR Benjamini/Yekutieli, Holm-Sidak, Simes-Hochberg, Hommel, FDR 2-stage Benjamini-Hochberg, FDR 2-stage Benjamini-Krieger-Yekutieli, FDR adaptive Gavrilov-Benjamini-Sarkar, Bonferroni, Sidak, and Holm.
- 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 map2slim.pl script supplied by geneontology.org)
Make sure your Python version >= 2.7, install the latest stable version via PyPI:
To install the development version:
pip install git+git://github.com/tanghaibao/goatools.git
.obo file for the most current GO:
.obo file for the most current GO Slim
terms (e.g. generic GOslim)
fisher (required) for calculating Fisher's exact test:
statsmodels (optional) for access to a variety of statistical tests for GOEA:
To plot the ontology lineage, install one of these two options:
Alternatively, it is possible to install via bioconda. See details
run.sh contains example cases, which calls the utility scripts in the
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
tests/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.
Important note: by default,
find_enrichment.py propagates counts to all
the parents of a GO term. As a result, users may find terms in the output that
are not present in their
association file. Use
disable this behavior.
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 command will plot the following image.
Sometimes people like to stylize the graph themselves, use option
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/plot_go_term.py --term=GO:0008135 --gml
map_to_slim.py for usage. As arguments it takes the gene ontology files:
- the current gene ontology file
- the GOslim file to be used (e.g.
goslim_generic.oboor any other GOslim file)
The script either maps one GO term to its GOslim terms, or protein products with multiple associations to all its GOslim terms.
To determine the GOslim terms for a single GO term, you can use the following command:
python scripts/map_to_slim.py --term=GO:0008135 go-basic.obo goslim_generic.obo
To determine the GOslim terms for protein products with multiple associations:
python scripts/map_to_slim.py --association_file=data/association go-basic.obo goslim_generic.obo
association file has the same format as used for
The implemented algorithm is described in more detail at the go-perl documentation of map2slim.
We have implemented several significance tests:
bonferroni, bonferroni correction
sidak, sidak correction
holm, hold correction
fdr, false discovery rate (fdr) implementation using resampling
Additional methods are available if
statsmodels is installed:
sm_bonferroni, bonferroni one-step correction
sm_sidak, sidak one-step correction
sm_holm-sidak, holm-sidak step-down method using Sidak adjustments
sm_holm, holm step-down method using Bonferroni adjustments
simes-hochberg, simes-hochberg step-up method (independent)
hommel, hommel closed method based on Simes tests (non-negative)
fdr_bh, fdr correction with Benjamini/Hochberg (non-negative)
fdr_by, fdr correction with Benjamini/Yekutieli (negative)
fdr_tsbh, two stage fdr correction (non-negative)
fdr_tsbky, two stage fdr correction (non-negative)
fdr_gbs, fdr adaptive Gavrilov-Benjamini-Sarkar
In total 15 tests are available, which can be selected using option
Please note that the default FDR (
fdr) uses a resampling strategy which may
lead to slightly different q-values between runs.
Haibao Tang et al. (2015). GOATOOLS: Tools for Gene Ontology. Zenodo. 10.5281/zenodo.31628.