{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T05:29:30Z","timestamp":1784179770358,"version":"3.55.0"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1012067","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2024,5,16]],"date-time":"2024-05-16T00:00:00Z","timestamp":1715817600000}}],"reference-count":59,"publisher":"Public Library of Science (PLoS)","issue":"5","license":[{"start":{"date-parts":[[2024,5,6]],"date-time":"2024-05-06T00:00:00Z","timestamp":1714953600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000057","name":"National Institute of General Medical Sciences","doi-asserted-by":"publisher","award":["GM143116-01"],"award-info":[{"award-number":["GM143116-01"]}],"id":[{"id":"10.13039\/100000057","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100015326","name":"Common Fund","doi-asserted-by":"publisher","award":["UM1HG011536"],"award-info":[{"award-number":["UM1HG011536"]}],"id":[{"id":"10.13039\/100015326","id-type":"DOI","asserted-by":"publisher"}]},{"name":"NIH","award":["R01 HG003143"],"award-info":[{"award-number":["R01 HG003143"]}]},{"name":"BWH","award":["6944620"],"award-info":[{"award-number":["6944620"]}]},{"DOI":"10.13039\/501100001822","name":"OeAW","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100001822","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>\n                    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\u2019 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\n                    <jats:italic>cooltools<\/jats:italic>\n                    (\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/open2c\/cooltools\" xlink:type=\"simple\">https:\/\/github.com\/open2c\/cooltools<\/jats:ext-link>\n                    ), a suite of computational tools that enables flexible, scalable, and reproducible analysis of high-resolution contact frequency data.\n                    <jats:italic>Cooltools<\/jats:italic>\n                    leverages the widely-adopted cooler format which handles storage and access for high-resolution datasets.\n                    <jats:italic>Cooltools<\/jats:italic>\n                    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,\n                    <jats:italic>cooltools<\/jats:italic>\n                    enables the effective use of the latest and largest genome folding datasets.\n                  <\/jats:p>","DOI":"10.1371\/journal.pcbi.1012067","type":"journal-article","created":{"date-parts":[[2024,5,6]],"date-time":"2024-05-06T13:59:58Z","timestamp":1715003998000},"page":"e1012067","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":210,"title":["Cooltools: Enabling high-resolution Hi-C analysis in 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