Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/tanghaibao/goatools
Python library to handle Gene Ontology (GO) terms
https://github.com/tanghaibao/goatools
bioinfomatics fdr fdr-benjamini fisher-tests gene-ontology gene-set-enrichment genomics goslim-terms holm-sidak sidak
Last synced: 13 days ago
JSON representation
Python library to handle Gene Ontology (GO) terms
- Host: GitHub
- URL: https://github.com/tanghaibao/goatools
- Owner: tanghaibao
- License: bsd-2-clause
- Created: 2010-01-15T19:15:21.000Z (almost 15 years ago)
- Default Branch: main
- Last Pushed: 2024-05-22T17:57:03.000Z (6 months ago)
- Last Synced: 2024-05-22T18:07:45.565Z (6 months ago)
- Topics: bioinfomatics, fdr, fdr-benjamini, fisher-tests, gene-ontology, gene-set-enrichment, genomics, goslim-terms, holm-sidak, sidak
- Language: Python
- Homepage:
- Size: 57.5 MB
- Stars: 730
- Watchers: 23
- Forks: 208
- Open Issues: 48
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
Awesome Lists containing this project
README
# GOATOOLS: A Python library for Gene Ontology analyses
[](https://pypi.python.org/pypi/goatools)
[](http://bioconda.github.io/recipes/goatools/README.html?highlight=goatools)
[](https://github.com/tanghaibao/goatools/actions)
[](https://pepy.tech/project/goatools)
| | |
| ------- | --------------------------------------------------------------------- |
| Authors | Haibao Tang ([tanghaibao](http://github.com/tanghaibao)) |
| | DV Klopfenstein ([dvklopfenstein](https://github.com/dvklopfenstein)) |
| | Brent Pedersen ([brentp](http://github.com/brentp)) |
| | Fidel Ramirez ([fidelram](https://github.com/fidelram)) |
| | Aurelien Naldi ([aurelien-naldi](http://github.com/aurelien-naldi)) |
| | Patrick Flick ([patflick](http://github.com/patflick)) |
| | Jeff Yunes ([yunesj](http://github.com/yunesj)) |
| | Kenta Sato ([bicycle1885](http://github.com/bicycle1885)) |
| | Chris Mungall ([cmungall](https://github.com/cmungall)) |
| | Greg Stupp ([stuppie](https://github.com/stuppie)) |
| | David DeTomaso ([deto](https://github.com/deto)) |
| | Olga Botvinnik ([olgabot](https://github.com/olgabot)) |
| Email | |
| License | BSD |
## How to cite
> [!TIP]
> GOATOOLS is now published in Scientific Reports!
>
> _Klopfenstein DV, ... Tang H (2018) GOATOOLS: A Python library for Gene
> Ontology analyses [Scientific reports](https://www.nature.com/articles/s41598-018-28948-z)_
- **GO Grouping**:
Visualize the major findings in a gene ontology enrichment analysis (GOEA) more easily with grouping.
A detailed description of GOATOOLS GO grouping is found in the
[manuscript](https://www.nature.com/articles/s41598-018-28948-z).
- **Compare GO lists**:
Compare [two](https://github.com/tanghaibao/goatools/issues/162) or more lists
of GO IDs using `compare_gos.py`, which 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](https://github.com/dvklopfenstein/goatools_simulation#manuscript-figures),
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)
## Contents
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](http://www.statsmodels.org/stable/index.html): 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](http://geneontology.org). The data structure is a directed
acyclic graph (DAG) that allows easy traversal from leaf to root.
- Read [GO Association files](http://geneontology.org/page/go-annotation-file-formats):
- GAF ([GO Annotation
File](http://geneontology.org/page/go-annotation-file-gaf-format-21))
- GPAD ([Gene Product Association Data](https://geneontology.github.io/docs/gene-product-association-data-gpad-format/))
- NCBI's gene2go file
- id2gos format. See [example](https://raw.githubusercontent.com/tanghaibao/goatools/main/data/association)
- [Print **_decendants count_** and/or **_information content_**](/notebooks/dcnt_and_tinfo.ipynb)
for a list of GO terms
- [Get parents or ancestors for a GO term with or without optional
relationships](notebooks/parents_and_ancestors.ipynb), including [Print details about a
GO ID's parents](notebooks/parent_go_terms.ipynb)
- Compare two or more lists of GO IDs
- [Plot GO hierarchies](https://github.com/tanghaibao/goatools#plot-go-lineage)
- [Write GO hierarchies to an ASCII text file](https://github.com/tanghaibao/goatools#write-go-hierarchy)
- 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)
## Installation
Make sure your Python version >= 3.7, and download an
`.obo` file of the most current
[GO](http://geneontology.org/docs/download-ontology):
```bash
wget http://current.geneontology.org/ontology/go-basic.obo
```
or `.obo` file for the most current [GO
Slim](http://geneontology.org/docs/go-subset-guide/) terms (e.g.
generic GOslim) :
```bash
wget http://current.geneontology.org/ontology/subsets/goslim_generic.obo
```
### [PyPI](https://pypi.org/project/goatools/)
```bash
pip install goatools
```
To install the development version:
```bash
pip install git+git://github.com/tanghaibao/goatools.git
```
### [Bioconda](http://bioconda.github.io/recipes/goatools/README.html?highlight=goatools)
```bash
conda install -c bioconda goatools
```
## Dependencies
When installing via PyPI or Bioconda as described above, all dependencies are automatically downloaded.
Alternatively, you can manually install:
- For statistical testing of GO enrichment:
- [`scipy.stats.fisher_exact`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.fisher_exact.html)
- `statsmodels` (optional) for access to a variety of statistical tests for GOEA
- To plot the ontology lineage, install one of these two options:
- [Graphviz](http://www.graphviz.org/), for graph visualization.
- [pygraphviz](http://networkx.lanl.gov/pygraphviz/), Python binding for communicating with Graphviz:
- [pydot](https://code.google.com/p/pydot/), a Python interface to Graphviz's Dot language.
## Cookbook
`run.sh` contains example cases, which calls the utility scripts in the
`scripts` folder.
### Find GO enrichment of genes under study
See examples in [find_enrichment](/doc/md/README_find_enrichment.md)
The `find_enrichment.py` 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:
```bash
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
- [`wr_hier.py`](doc/md/README_wr_hier.md): Given a GO ID, write the hierarchy below (default)
or above (`--up`) the given GO.
### Plot GO lineage
- [`go_plot.py`](doc/md/README_go_plot.md):
- 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` can plot the lineage
of a certain GO term, by:
```bash
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](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:
```bash
python scripts/plot_go_term.py --term=GO:0008135 --gml
```
### 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:
```bash
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:
```bash
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 implementation is similar to
[map2slim](http://search.cpan.org/~cmungall/go-perl/scripts/map2slim).
## Technical notes
### Available statistical tests for calculating uncorrected p-values
For calculating uncorrected p-values, we use SciPy:
- [`scipy.stats.fisher_exact`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.fisher_exact.html)
### 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.
## iPython [Notebooks](notebooks)
### Optional attributes
[definition](notebooks/Optional_attribute_definition.ipynb)
### Run a Ontology Enrichment Analysis (GOEA)
[goea_nbt3102](notebooks/goea_nbt3102.ipynb)
[human phenotype ontologies](notebooks/Enrichment_analyses_human_phenotype_ontology.ipynb)
### Show many study genes are associated with RNA, translation, mitochondria, and ribosomal
[goea_nbt3102_group_results](notebooks/goea_nbt3102_group_results.ipynb)
### Report level and depth counts of a set of GO terms
[report_depth_level](notebooks/report_depth_level.ipynb)
### Find all human protein-coding genes associated with cell cycle
[cell_cycle](notebooks/cell_cycle.ipynb)
### Calculate annotation coverage of GO terms on various species
[annotation_coverage](notebooks/annotation_coverage.ipynb)
### Determine the semantic similarities between GO terms
[semantic_similarity](notebooks/semantic_similarity.ipynb) [semantic_similarity_wang](notebooks/semantic_similarity_wang.ipynb)
### Obsolete GO terms are loaded upon request
[godag_obsolete_terms](notebooks/godag_obsolete_terms.ipynb)
## Want to Help?
Prior to submitting your pull request, please add a test which verifies your code, and run:
```console
make test
```
Items that we know we need include:
- Add code coverage runs
- Edit tests in the `makefile` under the comment
- Help setting up [documentation](http://goatools.readthedocs.io/en/latest/). 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
```
## Star History
[](https://star-history.com/#tanghaibao/goatools&Date)
Copyright (C) 2010-2021, Haibao Tang et al. All rights reserved.