Tools for Gene Ontology
|Authors||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:
Print decendants count and/or information content for a list of GO terms
Compare two or more lists of GO IDs using scripts/compare_gos.py
Group GO terms for easier viewing
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)
Please cite the following research paper if you use GOATOOLS in your research:
Klopfenstein DV, Zhang L, Pedersen BS, ... Tang H GOATOOLS: A Python library for Gene Ontology analyses Scientific reports | (2018) 8:10872 | DOI:10.1038/s41598-018-28948-z
- GO Grouping: Visualize the major findings in a gene ontology enrichment analysis (GEOA) more easily with grouping. A detailed description of GOATOOLS GO grouping is found in the manuscript. To group GO terms, see examples in find_enrichment examples where the optional --sections argument is used.
- Compare GO lists: Compare two or more lists of GO IDs using scripts/compare_gos.py. This script can be used with or without grouping.
- Stochastic GOEA simulations: One of the findings resulting from our simulations is: Larger study sizes result in higher GOEA sensitivity, meaning fewer truly significant observations go unreported. The code for the stochastic GOEA simulations described in the paper is found here: https://github.com/dvklopfenstein/goatools_simulation
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
.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:
statsmodels(optional) for access to a variety of statistical tests for GOEA:
To plot the ontology lineage, install one of these two options:
run.sh contains example cases, which calls the utility scripts in the
Find GO enrichment of genes under study
See examples in find_enrichment examples
find_enrichment.py for usage. It takes as arguments files
- gene names in a study
- gene names in population (or other study if
- 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.
Write GO hierarchy
- scripts/wr_hier.py: Given a GO ID, write the hierarchy below (default) or above (--up) the given GO.
Plot GO lineage
- Plots user-specified GO term(s) up to root
- Multiple user-specified GOs
- User-defined colors
- Plot relationships (-r)
- Optionally plot children of user-specfied GO terms
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
--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
Map GO terms to GOslim terms
map_to_slim.py for usage. As arguments it takes the gene ontology
- 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.
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.
fisher, fisher package's
fisher_scipy_stats:SciPy stats package fisher_exact
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?
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Copyright (C) 2010-2018, Haibao Tang et al. All rights reserved.