Cooler is a support library for a sparse, compressed, binary persistent storage format, also called cooler, used to store genomic interaction data, such as Hi-C contact matrices.

The cooler file format is an implementation of a genomic matrix data model using HDF5 as the container format. The cooler package includes a suite of command line tools and a Python API to facilitate creating, querying and manipulating cooler files.

To get started:

• Install cooler
• Read the documentation and see the Jupyter Notebook walkthrough.
• cool files from published Hi-C data sets are available at ftp://cooler.csail.mit.edu/coolers.
• Many more multires (mcool) files are available on the 4DN data portal.

Install from PyPI using pip.

$ pip install cooler

If you are using conda, you can alternatively install cooler from the bioconda channel.

$ conda install -c conda-forge -c bioconda cooler

Requirements:

• Python 2.7/3.4+
• libhdf5 and Python packages numpy, scipy, pandas, h5py. We highly recommend using the conda package manager to install scientific packages like these. To get it, you can either install the full Anaconda Python distribution or just the standalone conda package manager.

See the docs for more information.

NOTE: Python 2.7 support will sunset with cooler 0.8.

Interested in contributing to cooler? That's great! To get started, check out the contributing guide.

Abdennur, N., and Mirny, L. (2019). Cooler: scalable storage for Hi-C data and other genomically labeled arrays. Bioinformatics. doi: 10.1093/bioinformatics/btz540.

@article{Cooler2019,
    author = {Abdennur, Nezar and Mirny, Leonid A},
    title = "{Cooler: scalable storage for Hi-C data and other genomically labeled arrays}",
    journal = {Bioinformatics},
    year = {2019},
    month = {07},
    doi = {10.1093/bioinformatics/btz540},
    url = {https://doi.org/10.1093/bioinformatics/btz540},
}

BSD (3 Clause)

• Process Hi-C data with distiller!
• Downstream analysis with cooltools!
• Visualize your cooler data with HiGlass!