Cooltools: Enabling high-resolution Hi-C analysis in Python.

Autor: Abdennur N; Department of Genomics and Computational Biology, University of Massachusetts Chan Medical School, Worcester, Massachusetts, United States of America.; Department of Systems Biology, University of Massachusetts Chan Medical School, Worcester, Massachusetts, United States of America., Abraham S; Institute for Medical Engineering and Sciences, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts, United States of America., Fudenberg G; Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, California, United States of America., Flyamer IM; Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland., Galitsyna AA; Institute for Medical Engineering and Sciences, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts, United States of America., Goloborodko A; Institute of Molecular Biotechnology of the Austrian Academy of Sciences (IMBA), Vienna BioCenter (VBC), Vienna, Austria., Imakaev M; Institute for Medical Engineering and Sciences, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts, United States of America., Oksuz BA; Department of Systems Biology, University of Massachusetts Chan Medical School, Worcester, Massachusetts, United States of America., Venev SV; Department of Systems Biology, University of Massachusetts Chan Medical School, Worcester, Massachusetts, United States of America., Xiao Y; Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, California, United States of America.
Jazyk: angličtina
Zdroj: PLoS computational biology [PLoS Comput Biol] 2024 May 06; Vol. 20 (5), pp. e1012067. Date of Electronic Publication: 2024 May 06 (Print Publication: 2024).
DOI: 10.1371/journal.pcbi.1012067
Abstrakt: Chromosome conformation capture (3C) technologies reveal the incredible complexity of genome organization. Maps of increasing size, depth, and resolution are now used to probe genome architecture across cell states, types, and organisms. Larger datasets add challenges at each step of computational analysis, from storage and memory constraints to researchers' time; however, analysis tools that meet these increased resource demands have not kept pace. Furthermore, existing tools offer limited support for customizing analysis for specific use cases or new biology. Here we introduce cooltools (https://github.com/open2c/cooltools), a suite of computational tools that enables flexible, scalable, and reproducible analysis of high-resolution contact frequency data. Cooltools leverages the widely-adopted cooler format which handles storage and access for high-resolution datasets. Cooltools provides a paired command line interface (CLI) and Python application programming interface (API), which respectively facilitate workflows on high-performance computing clusters and in interactive analysis environments. In short, cooltools enables the effective use of the latest and largest genome folding datasets.
Competing Interests: The authors have declared that no competing interests exist.
(Copyright: © 2024 Open2C et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Databáze: MEDLINE
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