Tools for handling HiC and 5C data
Python C++
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Latest commit b2ff89e Jan 29, 2018


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This repository contains code for the hifive package, a set of tools for handling HiC and 5C data. This includes managing data from mapped reads, either in bam, mat, or raw formats. All stages use hdf5 dictionaries for fast access and minimal memory and storage usage.

This package includes methods for normalizing data from either HiC or 5C experiments at the fragment-end, or fragment level resolution, respectively. Once normalized, data can be used for plotting, binning, or other statistical tests within the package very quickly.

This package makes extensive use of the h5py and numpy packages. In addition, if mpi4py is installed, several methods are compatible with parallelization, including HiC normalization and heatmap generation.

Documentation can be found here.

The HiFive binary requires Python 2.7. However, the library can be used by Python 2.6 and 2.7.

Installing HiFive

The easiest way to get HiFive is using pip:

> pip install hifive

HiFive can also be obtained from github using the following command:

> git clone

or alternatively, download a snapshot of the repository using the following commands:

> wget
> tar -xf hifive_v1.0.tar

HiFive depends on a few packages and has several others that extend its functionality.

Required Packages

Recommended Packages

To install HiFive, simply enter the directory that the repository was cloned or downloaded to and use the following command:

> python install

If you wish to install HiFive in a location other than the default, you can use the prefix option:

> python install --prefix=/your/desired/path

Installing Documentation

In order to build HiFive's documentation locally, you need to execute the following command:

> cd doc
> make html

This will create the documentation suitable for viewing with a web browser. In order to create a pdf version of the documentation, simply run the following:

> cd doc
> make latexpdf