intervene 0.4.0

A tool for intersection and visualization of multiple genomic region sets

Introduction

Intervene is a tool for intersection and visualization of multiple genomic region and gene sets.

[Documentation](http://intervene.readthedocs.org)

Installation

Intervene requires the following Python modules and R packages:

• Python (=> 2.7 ): https://www.python.org/

• BedTools (Latest version): https://github.com/arq5x/bedtools2

• pybedtools (>= 0.7.9): https://daler.github.io/pybedtools/

• Pandas (>= 0.16.0): http://pandas.pydata.org/

• R (>= 3.0): https://www.r-project.org/

• R packages including UpSetR, corrplot

Install BEDTools

We are using pybedtools, which is Python wraper of BEDTools. So, BEDTools is should be installed before using Intervene.It’s recomended to have a latest version, but if you have an older version already install. Please read the instructions at https://github.com/arq5x/bedtools2 to install BEDTolls, and make sure the bedtools is on your path and you are able to call it from any directory.

Install required Python modules

If you already have a working installation of Python, the easiest way to install required Python modules 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 pybedtools

Install it from PyPi

pip install pybedtools

or using conda

conda install -c bioconda pybedtools

Read more details about ‘’pybedtools’’ installation: https://daler.github.io/pybedtools/main.html

Install Pandas

Install it from PyPi

pip install pandas

Or install with conda

conda install pandas

Install argparser

pip install argparser

Install required R packages

Intervene rquires two R packages, UpSetR https://cran.r-project.org/package=UpSetR and corrplot https://cran.r-project.org/package=corrplot for visualization.

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

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 GitHub

If you have git installed, use this:

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

intervene pairwise --help

intervene venn --help

intervene upset --help

Run Intervene on test data

To run Intervene using example data use the following command:

intervene pairwise --test

intervene venn --test

intervene upset --test

This will save the results in the current working directory with a folder named Intervene_test. If you wish to save the results in a specific folder, you can type:

intervene upset --test --output ~/path/to/your/folder/file_name.pdf

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. and Mathelier A., Intervene: a tool for intersection and visualization of multiple genomic region sets