Analyse genes against peak data, and vice versa


Keywords
RnaChipIntegrator
License
Artistic-2.0
Install
pip install RnaChipIntegrator==1.1.0

Documentation

RnaChipIntegrator: analysis of genes with peak data

https://readthedocs.org/projects/rnachipintegrator/badge/?version=latest https://travis-ci.org/fls-bioinformatics-core/RnaChipIntegrator.png?branch=master

RnaChipIntegrator is a utility that performs integrated analyses of 'gene' data (a set of genes or other genomic features) with 'peak' data (a set of regions, for example ChIP peaks) to identify the genes nearest to each peak, and vice versa.

The program was originally written specifically for ChIP-Seq and RNA-Seq data but works equally well for ChIP-chip and microarray expression data, and can also be used to integrate any set of genomic features (e.g. canonical genes, CpG islands) with peak data.

Quick Start

Install the latest version of the program from the Python Package Index (PyPI):

pip install RnaChipIntegrator

The simplest use of the program is:

RnaChipIntegrator GENES PEAKS

where GENES and PEAKS are tab-delimited files containing the 'gene' and 'peak' data respectively.

This will output two files with the nearest genes for each peak ("peak-centric" analysis), and the nearest peaks for each gene ("gene-centric" analysis).

Full documentation can be found at ReadTheDocs:

See the INSTALL file for complete installation instructions.

Developers

The source code for the development version of the program is hosted on GitHub in the devel branch:

and can be installed directly from GitHub using pip:

pip install git+https://github.com/fls-bioinformatics-core/RnaChipIntegrator.git@devel

The program depends on the Python xlwt, xlrd and xlutils libraries, which should be installed automatically if using pip.

Documentation based on sphinx is available under the docs directory.

To build do either:

python setup.py sphinx_build

or:

cd docs
make html

both of which create the documentation in the docs/_build subdirectory.

Running Tests

The Python unit tests can be run using:

python setup.py test

Note that this requires the nose package.

Examples

Example data files can be found in the examples subdirectory, which can be used as input to the program for test or demonstration purposes; see the README file in the same directory for more information.

Licensing

This software is licensed under the Artistic License 2.0; see the LICENSE document.