Getting started

The tools for your .cools

Chromosome conformation capture technologies reveal the incredible complexity of genome folding. A growing number of labs and multiple consortia, including the 4D Nucleome, the International Nucleome Consortium, and ENCODE, are generating higher-resolution datasets to probe genome architecture across cell states, types, and organisms. Larger datasets increase the challenges at each step of computational analysis, from storage, to memory, to researchers’ time. The recently-introduced cooler format readily handles storage of high-resolution datasets via a sparse data model.

cooltools leverages this format to enable flexible and reproducible analysis of high-resolution data. cooltools provides a suite of computational tools with a paired python API and command line access, which facilitates workflows either on high-performance computing clusters or via custom analysis notebooks. As part of the Open2C ecosystem, cooltools also provides detailed introductions to key concepts in Hi-C-data analysis with interactive notebook documentation.

If you use cooltools in your work, please cite cooltools via its zenodo DOI 10.5281/zenodo.5214125

Installation

Requirements

  • Python 3.7+

  • Scientific Python packages

Install using pip

Compile and install cooltools and its Python dependencies from PyPI using pip:

$ pip install cooltools

or install the latest version directly from github:

$ pip install https://github.com/open2c/cooltools/archive/refs/heads/master.zip

Install the development version

Finally, you can install the latest development version of cooltools from github. First, make a local clone of the github repository:

$ git clone https://github.com/open2c/cooltools

Then, you can compile and install cooltools in development mode, which installs the package without moving it to a system folder and thus allows immediate live-testing any changes in the python code.

$ cd cooltools
$ pip install -e ./

Note that these notebooks currently focus on mammalian interphase Hi-C analysis, but are readily extendible to other organisms and cellular contexts. To clone and work interactively with these notebooks, visit: https://github.com/open2c/open2c_examples.