{
    "info": {
        "author": "Haibao Tang, DV Klopfenstein",
        "author_email": "tanghaibao@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: BSD License",
            "Programming Language :: Python",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 3",
            "Topic :: Scientific/Engineering :: Bio-Informatics"
        ],
        "description": "# Tools for Gene Ontology\n\n[![DIO](/doc/images/DOI.svg)](https://www.nature.com/articles/s41598-018-28948-z)\n[![Latest PyPI version](https://img.shields.io/pypi/v/goatools.svg)](https://pypi.python.org/pypi/goatools)\n[![bioconda](https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg?style=flat)](http://bioconda.github.io/recipes/goatools/README.html?highlight=goatools)\n[![travisCI](https://travis-ci.org/tanghaibao/goatools.svg?branch=master)](https://travis-ci.org/tanghaibao/goatools)\n\n| | |\n|---|---|\n| Authors | Haibao Tang ([tanghaibao](http://github.com/tanghaibao)) |\n| | DV Klopfenstein ([dvklopfenstein](https://github.com/dvklopfenstein)) |\n| | Brent Pedersen ([brentp](http://github.com/brentp)) |\n| | Fidel Ramirez ([fidelram](https://github.com/fidelram)) |\n| | Aurelien Naldi ([aurelien-naldi](http://github.com/aurelien-naldi)) |\n| | Patrick Flick ([patflick](http://github.com/patflick)) |\n| | Jeff Yunes ([yunesj](http://github.com/yunesj)) |\n| | Kenta Sato ([bicycle1885](http://github.com/bicycle1885)) |\n| | Chris Mungall ([cmungall](https://github.com/cmungall)) |\n| | Greg Stupp ([stuppie](https://github.com/stuppie)) |\n| | David DeTomaso ([deto](https://github.com/deto)) |\n| | Olga Botvinnik ([olgabot](https://github.com/olgabot)) |\n| Email | <tanghaibao@gmail.com> |\n| License | BSD |\n\n## Description\n\nThis package contains a Python library to\n\n- Process over- and under-representation of certain GO terms, based on\n  Fisher's exact test. With numerous multiple correction routines\n  including locally implemented routines for Bonferroni, Sidak, Holm,\n  and false discovery rate. Also included are multiple test\n  corrections from\n  [statsmodels](http://www.statsmodels.org/stable/index.html): FDR\n  Benjamini/Hochberg, FDR Benjamini/Yekutieli, Holm-Sidak,\n  Simes-Hochberg, Hommel, FDR 2-stage Benjamini-Hochberg, FDR 2-stage\n  Benjamini-Krieger-Yekutieli, FDR adaptive Gavrilov-Benjamini-Sarkar,\n  Bonferroni, Sidak, and Holm.\n\n- Process the obo-formatted file from [Gene Ontology\n  website](http://geneontology.org). The data structure is a directed\n  acyclic graph (DAG) that allows easy traversal from leaf to root.\n\n- Read [GO Association files](http://geneontology.org/page/go-annotation-file-formats):\n  - GAF ([GO Annotation File](http://geneontology.org/page/go-annotation-file-gaf-format-21))\n  - GPAD ([Gene Product Association Data](https://geneontology.github.io/docs/gene-product-association-data-gpad-format/))\n  - NCBI's gene2go file\n  - id2gos format. See [example](https://raw.githubusercontent.com/tanghaibao/goatools/master/data/association)\n\n- [Print **_decendants count_** and/or **_information content_**](/notebooks/dcnt_and_tinfo.ipynb)\n  for a list of GO terms\n\n- Compare two or more lists of GO IDs using _scripts/compare_gos.py_\n- [Plot GO hierarchies](https://github.com/tanghaibao/goatools#plot-go-lineage)\n- [Write GO hierarchies to an ASCII text file](https://github.com/tanghaibao/goatools#write-go-hierarchy)\n- Group GO terms for easier viewing\n\n- Map GO terms (or protein products with multiple associations to\n  GO terms) to GOslim terms (analog to the map2slim.pl script supplied\n  by geneontology.org)\n\n## To Cite\n\n_Please cite the following research paper if you use GOATOOLS in your research_:\n\nKlopfenstein DV, Zhang L, Pedersen BS, ... Tang H [GOATOOLS: A Python library for Gene Ontology analyses](https://www.nature.com/articles/s41598-018-28948-z)\n_Scientific reports_ | (2018) 8:10872 | DOI:10.1038/s41598-018-28948-z\n\n  * **GO Grouping**:\n    Visualize the major findings in a gene ontology enrichment analysis (GEOA) more easily with grouping.\n    A detailed description of GOATOOLS GO grouping is found in the\n    [manuscript](https://www.nature.com/articles/s41598-018-28948-z).\n  * **Compare GO lists**:\n    Compare two or more lists of GO IDs using _scripts/compare_gos.py_.\n    This script can be used with or without grouping.\n  * **Stochastic GOEA simulations**:\n    One of the findings resulting from our simulations is:\n    [Larger study sizes result in higher GOEA sensitivity](https://github.com/dvklopfenstein/goatools_simulation#manuscript-figures),\n    meaning fewer truly significant observations go unreported.\n    The code for the stochastic GOEA simulations\n    described in the paper is found here:\n    https://github.com/dvklopfenstein/goatools_simulation\n\n## Installation\n\nMake sure your Python version >= 2.7, install the latest stable\nversion via PyPI:\n\n```bash\neasy_install goatools\n```\n\nTo install the development version:\n\n```bash\npip install git+git://github.com/tanghaibao/goatools.git\n```\n\n`.obo` file for the most current\n[GO](http://geneontology.org/page/download-ontology):\n\n```bash\nwget http://geneontology.org/ontology/go-basic.obo\n```\n\n`.obo` file for the most current [GO\nSlim](http://geneontology.org/page/go-slim-and-subset-guide) terms (e.g.\ngeneric GOslim) :\n\n```bash\nwget http://www.geneontology.org/ontology/subsets/goslim_generic.obo\n```\n\n## Dependencies\n\n- Simplest is to install via bioconda. See details\n   [here](http://bioconda.github.io/recipes/goatools/README.html?highlight=goatools).\n\n- To calculate the uncorrected p-values, there are currently twooptions:\n  - [fisher](http://pypi.python.org/pypi/fisher/) for calculating Fisher's exact test:\n\n  ```bash\n  easy_install fisher\n  ```\n\n  - [fisher](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.fisher_exact.html)\n    from [SciPy's](https://docs.scipy.org/doc/scipy/reference/)\n    [stats](https://docs.scipy.org/doc/scipy/reference/tutorial/stats.html) package\n\n  - `statsmodels` (optional) for access to a variety of statistical tests for GOEA:\n\n   ```bash\n   easy_install statsmodels\n   ```\n\n- To plot the ontology lineage, install one of these two options:\n  - Graphviz\n    - [Graphviz](http://www.graphviz.org/), for graph visualization.\n    - [pygraphviz](http://networkx.lanl.gov/pygraphviz/), Python binding for communicating with Graphviz:\n\n    ```bash\n    easy_install pygraphviz\n    ```\n\n  - [pydot](https://code.google.com/p/pydot/), a Python interface to Graphviz's Dot language.\n    - [pyparsing](http://pyparsing.wikispaces.com/) is a prerequisite for `pydot`\n    - Images can be viewed using either:\n      - [ImageMagick](http://www.imagemagick.org/)'s *display*\n      - [Graphviz](http://www.graphviz.org/)\n\n## Cookbook\n\n`run.sh` contains example cases, which calls the utility scripts in the\n`scripts` folder.\n\n### Find GO enrichment of genes under study\nSee examples in [find_enrichment examples](/doc/md/README_find_enrichment.md)\n\nSee `find_enrichment.py` for usage. It takes as arguments files\ncontaining:\n\n- gene names in a study\n- gene names in population (or other study if `--compare` is specified)\n- an association file that maps a gene name to a GO category.\n\nPlease look at `tests/data/` folder to see examples on how to make these\nfiles. when ready, the command looks like:\n\n```bash\npython scripts/find_enrichment.py --pval=0.05 --indent data/study \\\n                                  data/population data/association\n```\n\nand can filter on the significance of (e)nrichment or (p)urification. it\ncan report various multiple testing corrected p-values as well as the\nfalse discovery rate.\n\nThe \"e\" in the \"Enrichment\" column means \"enriched\" - the concentration\nof GO term in the study group is significantly *higher* than those in\nthe population. The \"p\" stands for \"purified\" - significantly *lower*\nconcentration of the GO term in the study group than in the population.\n\n**Important note**: by default, `find_enrichment.py` propagates counts\nto all the parents of a GO term. As a result, users may find terms in\nthe output that are not present in their `association` file. Use\n`--no_propagate_counts` to disable this behavior.\n\n### Write GO hierarchy\n  * [scripts/wr_hier.py](doc/md/README_wr_hier.md): Given a GO ID, write the hierarchy below (default)\n    or above (--up) the given GO.\n\n### Plot GO lineage\n\n  * [scripts/go_plot.py](doc/md/README_go_plot.md):\n    * Plots user-specified GO term(s) up to root\n    * Multiple user-specified GOs\n    * User-defined colors\n    * Plot relationships (-r)\n    * Optionally plot children of user-specfied GO terms\n  * [scripts/plot_go_term.py](plot_go_term-py)\n\n\n#### plot_go_term.py\n\nSee `plot_go_term.py` for usage. `plot_go_term.py` can plot the lineage\nof a certain GO term, by:\n\n```bash\npython scripts/plot_go_term.py --term=GO:0008135\n```\n\nThis command will plot the following image.\n\n![GO term lineage](https://www.dropbox.com/s/4zbqx8sqcls3mge/gograph.png?raw=1)\n\nSometimes people like to stylize the graph themselves, use option\n`--gml` to generate a GML output which can then be used in an external\ngraph editing software like [Cytoscape](http://www.cytoscape.org/). The\nfollowing image is produced by importing the GML file into Cytoscape\nusing yFile orthogonal layout and solid VizMapping. Note that the [GML\nreader plugin](https://code.google.com/p/graphmlreader/) may need to be\ndownloaded and installed in the `plugins` folder of Cytoscape:\n\n```bash\npython scripts/plot_go_term.py --term=GO:0008135 --gml\n```\n\n![GO term lineage (Cytoscape)](https://www.dropbox.com/s/ueov2ioxl063q8h/gograph-gml.png?raw=1)\n\n### Map GO terms to GOslim terms\n\nSee `map_to_slim.py` for usage. As arguments it takes the gene ontology\nfiles:\n\n- the current gene ontology file `go-basic.obo`\n- the GOslim file to be used (e.g. `goslim_generic.obo` or any other GOslim file)\n\nThe script either maps one GO term to its GOslim terms, or protein\nproducts with multiple associations to all its GOslim terms.\n\nTo determine the GOslim terms for a single GO term, you can use the\nfollowing command:\n\n```bash\npython scripts/map_to_slim.py --term=GO:0008135 go-basic.obo goslim_generic.obo\n```\n\nTo determine the GOslim terms for protein products with multiple\nassociations:\n\n```bash\npython scripts/map_to_slim.py --association_file=data/association go-basic.obo goslim_generic.obo\n```\n\nWhere the `association` file has the same format as used for\n`find_enrichment.py`.\n\nThe implemented algorithm is described in more detail at the go-perl\ndocumentation of\n[map2slim](http://search.cpan.org/~cmungall/go-perl/scripts/map2slim).\n\n## Technical notes\n\n### Available statistical tests for calculating uncorrected p-values\n\nThere are currently two fisher tests available for calculating uncorrected\np-values. Both fisher options from the fisher package and SciPy's stats package\ncalculate the same pvalues, but provide the user an option in installing\npackages.\n\n- `fisher`, [fisher](http://pypi.python.org/pypi/fisher/) package's `fisher.pvalue_population`\n- `fisher_scipy_stats`:[SciPy](https://docs.scipy.org/doc/scipy/reference/)\n   [stats](https://docs.scipy.org/doc/scipy/reference/tutorial/stats.html) package\n  [fisher_exact](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.fisher_exact.html)\n\n### Available multiple test corrections\n\nWe have implemented several significance tests:\n\n- `bonferroni`, bonferroni correction\n- `sidak`, sidak correction\n- `holm`, hold correction\n- `fdr`, false discovery rate (fdr) implementation using resampling\n\nAdditional methods are available if `statsmodels` is installed:\n\n- `sm_bonferroni`, bonferroni one-step correction\n- `sm_sidak`, sidak one-step correction\n- `sm_holm-sidak`, holm-sidak step-down method using Sidak adjustments\n- `sm_holm`, holm step-down method using Bonferroni adjustments\n- `simes-hochberg`, simes-hochberg step-up method (independent)\n- `hommel`, hommel closed method based on Simes tests (non-negative)\n- `fdr_bh`, fdr correction with Benjamini/Hochberg (non-negative)\n- `fdr_by`, fdr correction with Benjamini/Yekutieli (negative)\n- `fdr_tsbh`, two stage fdr correction (non-negative)\n- `fdr_tsbky`, two stage fdr correction (non-negative)\n- `fdr_gbs`, fdr adaptive Gavrilov-Benjamini-Sarkar\n\nIn total 15 tests are available, which can be selected using option\n`--method`. Please note that the default FDR (`fdr`) uses a resampling\nstrategy which may lead to slightly different q-values between runs.\n\n## iPython Notebooks\n\n### Run a Gene Ontology Enrichment Analysis (GOEA)\n\n<https://github.com/tanghaibao/goatools/blob/master/notebooks/goea_nbt3102.ipynb>\n\n### Show many study genes are associated with RNA, translation, mitochondria, and ribosomal\n\n<https://github.com/tanghaibao/goatools/blob/master/notebooks/goea_nbt3102_group_results.ipynb>\n\n### Report level and depth counts of a set of GO terms\n\n<https://github.com/tanghaibao/goatools/blob/master/notebooks/report_depth_level.ipynb>\n\n### Find all human protein-coding genes associated with cell cycle\n\n<https://github.com/tanghaibao/goatools/blob/master/notebooks/cell_cycle.ipynb>\n\n### Calculate annotation coverage of GO terms on various species\n\n<https://github.com/tanghaibao/goatools/blob/master/notebooks/annotation_coverage.ipynb>\n\n### Determine the semantic similarities between GO terms\n\n<https://github.com/tanghaibao/goatools/blob/master/notebooks/semantic_similarity.ipynb>\n\n## Want to Help?\n\nPrior to submitting your pull request, please add a test which verifies your code, and run:\n```\nmake test\n```\n\nItems that we know we need include:\n\n- Add code coverage runs\n- Edit tests in the `makefile` under the comment, `# TBD`, suchthey run using `nosetests`\n- Help setting up [documentation](http://goatools.readthedocs.io/en/latest/). We\n  are using Sphinx and Python docstrings to create documentation.\n  For documentation practice, use make targets:\n\n  ```bash\n  make mkdocs_practice\n  ```\n  To remove practice documentation:\n\n  ```bash\n  make rmdocs_practice\n  ```\n\n  Once you are happy with the documentation do:\n\n  ```bash\n  make gh-pages\n  ```\n\nCopyright (C) 2010-2018, Haibao Tang et al. All rights reserved.",
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