Cooler: scalable storage for Hi-C data and other genomically-labeled arrays

By Nezar Abdennur, Leonid A. Mirny. bioRxiv, 2019

1,588 citations recorded by the source corpus.

Most existing coverage-based (epi)genomic datasets are one-dimensional, but newer technologies probing interactions (phys-ical, genetic, etc.) produce quantitative maps with two-dimensional genomic coordinate systems. Storage and compu-tational costs mount sharply with data resolution when such maps are stored in dense form. Hence, there is a pressing need to develop data storage strategies that handle the full range of useful resolutions in multidimensional genomic datasets by tak-ing advantage of their sparse nature, while supporting efficient compression and providing fast random access to facilitate development of scalable algorithms for data analysis. We devel-oped a file format called cooler, based on a sparse data model, that can support genomically-labeled matrices at any resolution. It has the flexibility to accommodate various descriptions of the data axes (genomic coordinates, tracks and bin annotations), resolutions, data density patterns, and metadata. Cooler is based on HDF5 and is supported by a Python library and command line suite to create, read, inspect and manipulate cooler data collections. The format has been adopted as a standard by the NIH 4D Nucleome Consortium. Cooler is cross-platform, BSD-licensed, and can be installed from the Python Package In-dex or the bioconda repository. The source code is maintained on Github at https://github.com/mirnylab/cooler

Subfields: Genetics, Molecular Biology, Plant Science

View Paper: https://www.semanticscholar.org/paper/399f524be265b524336576658a3cce34324adbd4

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Automated assembly scaffolding using RagTag elevates a new tomato system for high-throughput genome editing (2022, positive): “Cooler and HiGlass were used to visualize Hi-C heatmaps [53, 54].”