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- Title
Cooltools: Enabling high-resolution Hi-C analysis in Python.
- Authors
Abdennur, Nezar; Abraham, Sameer; Fudenberg, Geoffrey; Flyamer, Ilya M.; Galitsyna, Aleksandra A.; Goloborodko, Anton; Imakaev, Maxim; Oksuz, Betul A.; Venev, Sergey V.; Xiao, Yao
- 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. Author summary: Chromosome conformation capture (3C) experiments, including Hi-C, measure the 3D organization of chromosomes inside cells. As 3C datasets grow larger and higher-resolution, analyzing them poses computational challenges. Our open-source Python package cooltools meets these challenges. Cooltools integrates smoothly with cooler, a software library for storing and accessing very large Hi-C datasets. Cooltools enables users to extract key features seen in 3C maps, including: the decay of contact frequency with genomic distance, plaid patterns of active and inactive compartments, domains, and dots. Cooltools quantifies these folding features from raw contact data efficiently, handling chromosome-scale datasets too large to fit in memory. The command-line and Python interfaces make cooltools easy to integrate into pipelines or customized for new analyses. By overcoming data size and memory hurdles, cooltools allows researchers to harness 3C's full potential for understanding principles of genome folding and function.
- Subjects
PYTHON programming language; PYTHONS; COMPUTER workstation clusters; RESEARCH personnel; CHROMOSOMES; CLUSTER analysis (Statistics)
- Publication
PLoS Computational Biology, 2024, Vol 20, Issue 5, p1
- ISSN
1553-734X
- Publication type
Article
- DOI
10.1371/journal.pcbi.1012067