Skip to main content

A tool for intersection and visualization of multiple gene or genomic region sets

Project description

https://raw.githubusercontent.com/asntech/intervene/master/docs/img/intervene_logo.png

Intervene

a tool for intersection and visualization of multiple gene or genomic region sets

https://travis-ci.org/asntech/intervene.svg?branch=master https://badge.fury.io/py/intervene.svg https://img.shields.io/github/issues/asntech/intervene.svg sphinx documentation for latest release https://img.shields.io/twitter/url/https/github.com/asntech/intervene.svg?style=social

Documentation

A detailed documentation is available in different formats: HTML | PDF | ePUB

Installation

Intervene requires the following Python modules and R packages:

Install BEDTools

We are using pybedtools, which is Python wrapper for BEDTools. So, BEDTools should be installed before using Intervene. It’s recomended to have a latest version, but if you have an older version already install, it should be fine.

A quick installation, if you have conda installed.

conda install -c bioconda bedtools

Please read the instructions at https://github.com/arq5x/bedtools2 to install BEDTools, and make sure it is on your path and you are able to call bedtools from any directory.

Install required Python modules

Intervene takes care of the installation of all the required Python modules. If you already have a working installation of Python, the easiest way to install Intervene is by using pip. If you’re setting up Python for the first time, we recommend to install it using Anaconda Python distribution http://continuum.io/downloads. These come with several helpful scientific and data processing libraries. These are available for platforms including Windows, Mac OSX and Linux.

Install required R packages

Intervene rquires three R packages, UpSetR , corrplot for visualization and Cairo to generate high-quality vector and bitmap figures.

install.packages(c("UpSetR", "corrplot", "Cairo"))

Install Intervene

You can install a stable version of Intervene by using pip from PyPi or a development version by using git from GitHub.

Install using pip

You can install InterVene either from PyPi using pip or install it from the source. Please make sure you have already installed the above mentioned python libraries required to run InterVene.

Install from PyPi:

pip install intervene

Install development version from Bitbucket

If you have git installed, use this:

git clone https://bitbucket.org/CBGR/intervene.git
cd intervene
python setup.py sdist install

Install development version from GitHub

If you have git installed, use this:

git clone https://github.com/asntech/intervene.git
cd intervene
python setup.py sdist install

How to use Intervene

Once you have installed Intervene, you can type:

intervene --help

usage: intervene <subcommand> [options]

positional arguments <subcommand>:
  {venn,upset,pairwise}
                        List of subcommands
    venn                Venn diagram of intersection of genomic regions or list sets (upto 6-way).
    upset               UpSet diagram of intersection of genomic regions or list sets.
    pairwise            Pairwise intersection and heatmap of N genomic region sets in <BED/GTF/GFF> format.

optional arguments:
  -h, --help            show this help message and exit
  -v, --version         show program's version number and exit

to see the help for the three subcommands pairwise, venn and upset type:

.. code-block:: bash

intervene pairwise –help

intervene venn –help

intervene upset –help

Run Intervene on test data

To run Intervene using example data, use the following commands. To access the test data make sure you have sudo or root access.

intervene pairwise --test

intervene venn --test

intervene upset --test

If you have installed Intervene locally from the source code, you may have problem to find test data. You can download the test data here https://github.com/asntech/intervene/tree/master/intervene/example_data and point to it using -i instead of --test.

./intervene/intervene venn -i intervene/example_data/ENCODE_hESC/*.bed
./intervene/intervene upset -i intervene/example_data/ENCODE_hESC/*.bed
./intervene/intervene pairwise -i intervene/example_data/dbSUPER_mm9/*.bed

The above three test commands will generate the following three figures (a, b and c).

http://intervene.readthedocs.io/en/latest/_images/Intervene_plots.png

By default your results will stored in the current working directory with a folder named Intervene_results. If you wish to save the results in a specific folder, you can type:

intervene upset --test --output ~/path/to/your/folder

Interactive Shiny App

Intervene Shiny App is freely available at https://asntech.shinyapps.io/intervene

Support

If you have questions, or found any bug in the program, please write to us at aziz.khan[at]ncmm.uio.no

Cite Us

If you use Intervene please cite us: Khan A, Mathelier A: Intervene: a tool for intersection and visualization of multiple gene or genomic region sets. bioRxiv 2017, doi: https://doi.org/10.1101/109728

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

intervene-0.5.7.tar.gz (5.5 MB view details)

Uploaded Source

Built Distribution

intervene-0.5.7-py2.7.egg (5.6 MB view details)

Uploaded Source

File details

Details for the file intervene-0.5.7.tar.gz.

File metadata

  • Download URL: intervene-0.5.7.tar.gz
  • Upload date:
  • Size: 5.5 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No

File hashes

Hashes for intervene-0.5.7.tar.gz
Algorithm Hash digest
SHA256 06c027fced0140daa3f1b40b6146a66670096baa08327830ebb5671ae01aebd6
MD5 34d99f721dd5f85ffc06f1acf191ab01
BLAKE2b-256 ba2296716723791a370d09f5ee734f2c1b603df8028f1e9ebad4ea0b6f60b6b7

See more details on using hashes here.

File details

Details for the file intervene-0.5.7-py2.7.egg.

File metadata

File hashes

Hashes for intervene-0.5.7-py2.7.egg
Algorithm Hash digest
SHA256 73116156d129e4986f9d279fbc24d0bb6aee4d291931ec47354ffe3baad14fe7
MD5 5832630866134c08cbe2b558e08528d7
BLAKE2b-256 266b0f46a46abcdba177f73ba5d9838bc98e219b2c6c88451f787a3b474ac0f7

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page