Python scripts to find enrichment of GO terms
|Author||Haibao Tang (tanghaibao)|
|DV Klopfenstein (dvklopfenstein)|
|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)|
|David DeTomaso (deto)|
|Olga Botvinnik (olgabot)|
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.
- Read GO Association files:
- Read GAF (Gene Association File) files.
- Read NCBI’s gene2go GO association file.
- 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) :
- Simplest is to install via bioconda. See details here.
- To calculate the uncorrected p-values, there are currently twooptions:
- fisher for calculating Fisher’s exact test:
bash easy_install fisher
- fisher from SciPy’s stats package
- statsmodels (optional) for access to a variety of statistical tests for GOEA:
bash easy_install statsmodels
To plot the ontology lineage, install one of these two options:
pydot, a Python interface to Graphviz’s Dot language.
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 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 --no_propagate_counts to disable this behavior.
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 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/plot_go_term.py --term=GO:0008135 --gml
GO term lineage (Cytoscape)
Map GO terms to GOslim terms
See map_to_slim.py for usage. As arguments it takes the gene ontology files:
- the current gene ontology file go-basic.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 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
Where the association file has the same format as used for find_enrichment.py.
The implemented algorithm is described in more detail at the go-perl documentation of map2slim.
Available statistical tests for calculating uncorrected p-values
There are currently two fisher tests available for calculating uncorrected p-values. Both fisher options from the fisher package and SciPy’s stats package calculate the same pvalues, but provide the user an option in installing packages.
Available multiple test corrections
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 --method. Please note that the default FDR (fdr) uses a resampling strategy which may lead to slightly different q-values between runs.
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
Want to Help?
Prior to submitting your pull request, please add a test which verifies your code, and run:
Items that we know we need include:
- Add code coverage runs
- Edit tests in the makefile under the comment, # TBD, suchthey run using nosetests
- Help setting up documentation. We are using Sphinx and Python docstrings to create documentation. For documentation practice, use make targets:
bash make mkdocs_practice To remove practice documentation:
bash make rmdocs_practice
Once you are happy with the documentation do:
bash make gh-pages
Copyright (C) 2010-2018. Haibao Tang et al. GOATOOLS: Tools for Gene Ontology. Zenodo. 10.5281/zenodo.31628.