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

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Open2C, Abdennur, Nezar, Abraham, Sameer, Fudenberg, Geoffrey, Flyamer, Ilya M., Galitsyna, Aleksandra A., Goloborodko, Anton, Imakaev, Maxim +3 more·2024·PLOS Computational Biology
DOI 10.1371/journal.pcbi.1012067PMC PMC11098495

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Abstract

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.

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  • Independent Hi-C replicates (up to two) were merged for each condition only after ascertaining overall similarity of each replicate using P(s) curves and aggregate peak analysis.

    “After ascertaining the overall similarity of each replicate using either cooltools (86) or Genova (87) [P(s) curves and aggregate peak analysis], the independent Hi-C replicates (up to two) were merged for each condition.”

    From Synergy between regulatory elements can render cohesin dispensable for distal enhancer function (2026) · cites this paper as [86]

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Authors
Open2C, Abdennur, N., Abraham, S., Fudenberg, G., Flyamer, I. M., Galitsyna, A. A., Goloborodko, A., Imakaev, M., Oksuz, B. A., Venev, S. V., & Xiao, Y.
Affiliations
  • University of Massachusetts Chan Medical School (US)
  • University of Southern California (US)
  • Massachusetts Institute of Technology (US)
  • Institute of Molecular Biotechnology (AT)
Corresponding author
  • Open2C
  • Abdennur, Nezar
  • Fudenberg, Geoffrey
  • Flyamer, Ilya M.
  • Galitsyna, Aleksandra A.
  • Goloborodko, Anton
  • Venev, Sergey V.
  • Xiao, Yao
ORCID
Venue
PLOS Computational Biology
Publisher
Public Library of Science (PLoS)
Published
2024-05-06
Volume
20
Issue
5
Pages
e1012067
ISSN
  • 1553-734X (ISSN-L)
  • 1553-7358
DOI
10.1371/journal.pcbi.1012067
PMC
PMC11098495
OpenAlex
W4396667339
Cited by
313
References
8
Citations by year
2026: 107 · 2025: 102 · 2024: 72 · 2023: 30 · 2022: 2
FWCI
38.11
Type
journal article
Language
en
Update notices
new_version (2024-05-16)
Open access
Licence
Locations
  • PLoS Computational Biology (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • National Institute of General Medical Sciences (GM143116-01)
  • Common Fund (UM1HG011536)
  • NIH (R01 HG003143)
  • BWH (6944620)
  • OeAW
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomic variations and chromosomal abnormalities
Keywords
Hi-C, chromosome conformation capture, genome organization, Python interface, command-line interface, reproducible analysis, high-performance computing
MeSH terms
Chromosome Mapping, Humans, Programming Languages, Software, Genome, Computational Biology, Genomics

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