PHi-C2: interpreting Hi-C data as the dynamic 3D genome state.


Journal

Bioinformatics (Oxford, England)
ISSN: 1367-4811
Titre abrégé: Bioinformatics
Pays: England
ID NLM: 9808944

Informations de publication

Date de publication:
31 10 2022
Historique:
received: 07 05 2022
revised: 14 08 2022
pubmed: 11 9 2022
medline: 3 11 2022
entrez: 10 9 2022
Statut: ppublish

Résumé

High-throughput chromosome conformation capture (Hi-C) is a widely used assay for studying the three-dimensional (3D) genome organization across the whole genome. Here, we present PHi-C2, a Python package supported by mathematical and biophysical polymer modeling that converts input Hi-C matrix data into the polymer model's dynamics, structural conformations and rheological features. The updated optimization algorithm for regenerating a highly similar Hi-C matrix provides a fast and accurate optimal solution compared to the previous version by eliminating the factors underlying the inefficiency of the optimization algorithm in the iterative optimization process. In addition, we have enabled a Google Colab workflow to run the algorithm, wherein users can easily change the parameters and check the results in the notebook. Overall, PHi-C2 represents a valuable tool for mining the dynamic 3D genome state embedded in Hi-C data. PHi-C2 as the phic Python package is freely available under the GPL license and can be installed from the Python package index. The source code is available from GitHub at https://github.com/soyashinkai/PHi-C2. Moreover, users do not have to prepare a Python environment because PHi-C2 can run on Google Colab (https://bit.ly/3rlptGI). Supplementary data are available at Bioinformatics online.

Identifiants

pubmed: 36087002
pii: 6695219
doi: 10.1093/bioinformatics/btac613
pmc: PMC9620818
doi:

Substances chimiques

Polymers 0

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

4984-4986

Informations de copyright

© The Author(s) 2022. Published by Oxford University Press.

Références

Science. 2009 Oct 9;326(5950):289-93
pubmed: 19815776
J Mol Graph. 1996 Feb;14(1):33-8, 27-8
pubmed: 8744570
Biophys J. 2020 May 5;118(9):2220-2228
pubmed: 32191860
Cell Syst. 2018 Feb 28;6(2):256-258.e1
pubmed: 29428417
Comput Struct Biotechnol J. 2020 Aug 21;18:2259-2269
pubmed: 32952939
Nat Genet. 2018 May;50(5):662-667
pubmed: 29662163
Cell Syst. 2016 Jul;3(1):99-101
pubmed: 27467250
PLoS Comput Biol. 2021 Dec 6;17(12):e1009669
pubmed: 34871311
NAR Genom Bioinform. 2020 Mar 31;2(2):lqaa020
pubmed: 33575580
Science. 2001 Dec 7;294(5549):2181-6
pubmed: 11739961
Genome Biol. 2018 Aug 24;19(1):125
pubmed: 30143029
Cell. 2017 Oct 19;171(3):557-572.e24
pubmed: 29053968
J Cell Biol. 2019 May 6;218(5):1511-1530
pubmed: 30824489

Auteurs

Soya Shinkai (S)

Laboratory for Developmental Dynamics, RIKEN Center for Biosystems Dynamics Research, Kobe 650-0047, Japan.

Hiroya Itoga (H)

Laboratory for Developmental Dynamics, RIKEN Center for Biosystems Dynamics Research, Kobe 650-0047, Japan.

Koji Kyoda (K)

Laboratory for Developmental Dynamics, RIKEN Center for Biosystems Dynamics Research, Kobe 650-0047, Japan.

Shuichi Onami (S)

Laboratory for Developmental Dynamics, RIKEN Center for Biosystems Dynamics Research, Kobe 650-0047, Japan.
Life Science Data Sharing Unit, Infrastructure Research and Development Division, RIKEN Information R&D and Strategy Headquarters, Kobe 650-0047, Japan.

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Classifications MeSH