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

Abstract Motivation Most existing coverage-based (epi)genomic datasets are one-dimensional, but newer technologies probing interactions (physical, genetic, etc.) produce quantitative maps with two-dimensional genomic coordinate systems. Storage and computational costs mount sharply with data resolut...

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Bibliographic Details
Published in	Bioinformatics Vol. 36; no. 1; pp. 311 - 316
Main Authors	Abdennur, Nezar, Mirny, Leonid A
Format	Journal Article
Language	English
Published	England Oxford University Press 01.01.2020
Subjects	Algorithms Genomics - methods Information Storage and Retrieval Metadata Original Papers Software
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Abstract	Abstract Motivation Most existing coverage-based (epi)genomic datasets are one-dimensional, but newer technologies probing interactions (physical, genetic, etc.) produce quantitative maps with two-dimensional genomic coordinate systems. Storage and computational 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 taking advantage of their sparse nature, while supporting efficient compression and providing fast random access to facilitate development of scalable algorithms for data analysis. Results We developed 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. Availability and implementation Cooler is cross-platform, BSD-licensed and can be installed from the Python package index or the bioconda repository. The source code is maintained on Github at https://github.com/mirnylab/cooler. Supplementary information Supplementary data are available at Bioinformatics online.
AbstractList	Abstract Motivation Most existing coverage-based (epi)genomic datasets are one-dimensional, but newer technologies probing interactions (physical, genetic, etc.) produce quantitative maps with two-dimensional genomic coordinate systems. Storage and computational 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 taking advantage of their sparse nature, while supporting efficient compression and providing fast random access to facilitate development of scalable algorithms for data analysis. Results We developed 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. Availability and implementation Cooler is cross-platform, BSD-licensed and can be installed from the Python package index or the bioconda repository. The source code is maintained on Github at https://github.com/mirnylab/cooler. Supplementary information Supplementary data are available at Bioinformatics online. Most existing coverage-based (epi)genomic datasets are one-dimensional, but newer technologies probing interactions (physical, genetic, etc.) produce quantitative maps with two-dimensional genomic coordinate systems. Storage and computational 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 taking advantage of their sparse nature, while supporting efficient compression and providing fast random access to facilitate development of scalable algorithms for data analysis.MOTIVATIONMost existing coverage-based (epi)genomic datasets are one-dimensional, but newer technologies probing interactions (physical, genetic, etc.) produce quantitative maps with two-dimensional genomic coordinate systems. Storage and computational 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 taking advantage of their sparse nature, while supporting efficient compression and providing fast random access to facilitate development of scalable algorithms for data analysis.We developed 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.RESULTSWe developed 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 index or the bioconda repository. The source code is maintained on Github at https://github.com/mirnylab/cooler.AVAILABILITY AND IMPLEMENTATIONCooler is cross-platform, BSD-licensed and can be installed from the Python package index or the bioconda repository. The source code is maintained on Github at https://github.com/mirnylab/cooler.Supplementary data are available at Bioinformatics online.SUPPLEMENTARY INFORMATIONSupplementary data are available at Bioinformatics online. Most existing coverage-based (epi)genomic datasets are one-dimensional, but newer technologies probing interactions (physical, genetic, etc.) produce quantitative maps with two-dimensional genomic coordinate systems. Storage and computational 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 taking advantage of their sparse nature, while supporting efficient compression and providing fast random access to facilitate development of scalable algorithms for data analysis. We developed 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 index or the bioconda repository. The source code is maintained on Github at https://github.com/mirnylab/cooler. Supplementary data are available at Bioinformatics online.
Author	Abdennur, Nezar Mirny, Leonid A
AuthorAffiliation	2 Department of Physics, Massachusetts Institute of Technology , Cambridge, MA 02139, USA 1 Institute for Medical Engineering and Science, Cambridge, MA 02139, USA
AuthorAffiliation_xml	– name: 2 Department of Physics, Massachusetts Institute of Technology , Cambridge, MA 02139, USA – name: 1 Institute for Medical Engineering and Science, Cambridge, MA 02139, USA
Author_xml	– sequence: 1 givenname: Nezar orcidid: 0000-0001-5814-0864 surname: Abdennur fullname: Abdennur, Nezar email: nezar@mit.edu organization: Institute for Medical Engineering and Science, Cambridge, MA 02139, USA – sequence: 2 givenname: Leonid A surname: Mirny fullname: Mirny, Leonid A email: leonid@mit.edu organization: Institute for Medical Engineering and Science, Cambridge, MA 02139, USA
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/31290943$$D View this record in MEDLINE/PubMed
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Snippet	Abstract Motivation Most existing coverage-based (epi)genomic datasets are one-dimensional, but newer technologies probing interactions (physical, genetic,... Most existing coverage-based (epi)genomic datasets are one-dimensional, but newer technologies probing interactions (physical, genetic, etc.) produce...
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Title	Cooler: scalable storage for Hi-C data and other genomically labeled arrays
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