PHi-C: deciphering Hi-C data into polymer dynamics.
Journal
NAR genomics and bioinformatics
ISSN: 2631-9268
Titre abrégé: NAR Genom Bioinform
Pays: England
ID NLM: 101756213
Informations de publication
Date de publication:
Jun 2020
Jun 2020
Historique:
received:
18
07
2019
revised:
26
02
2020
accepted:
13
03
2020
entrez:
12
2
2021
pubmed:
13
2
2021
medline:
13
2
2021
Statut:
epublish
Résumé
Genomes are spatiotemporally organized within the cell nucleus. Genome-wide chromosome conformation capture (Hi-C) technologies have uncovered the 3D genome organization. Furthermore, live-cell imaging experiments have revealed that genomes are functional in 4D. Although computational modeling methods can convert 2D Hi-C data into population-averaged static 3D genome models, exploring 4D genome nature based on 2D Hi-C data remains lacking. Here, we describe a 4D simulation method, PHi-C (polymer dynamics deciphered from Hi-C data), that depicts 4D genome features from 2D Hi-C data by polymer modeling. PHi-C allows users to interpret 2D Hi-C data as physical interaction parameters within single chromosomes. The physical interaction parameters can then be used in the simulations and analyses to demonstrate dynamic characteristics of genomic loci and chromosomes as observed in live-cell imaging experiments. PHi-C is available at https://github.com/soyashinkai/PHi-C.
Identifiants
pubmed: 33575580
doi: 10.1093/nargab/lqaa020
pii: lqaa020
pmc: PMC7671433
doi:
Types de publication
Journal Article
Langues
eng
Pagination
lqaa020Informations de copyright
© The Author(s) 2019. Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics.
Références
Mol Cell. 2017 Jul 20;67(2):282-293.e7
pubmed: 28712725
Nucleic Acids Res. 2015 Oct 30;43(19):e127
pubmed: 26092696
FEBS Lett. 2015 Oct 7;589(20 Pt A):2987-95
pubmed: 25980604
Cell. 2019 Jan 24;176(3):520-534.e25
pubmed: 30661750
PLoS Comput Biol. 2016 Oct 20;12(10):e1005136
pubmed: 27764097
Biophys J. 2018 Dec 18;115(12):2286-2294
pubmed: 30527448
Proc Natl Acad Sci U S A. 2017 Nov 14;114(46):12126-12131
pubmed: 29087948
Genome Biol. 2017 Jan 30;18(1):21
pubmed: 28137286
PLoS Comput Biol. 2017 Jul 19;13(7):e1005665
pubmed: 28723903
Nat Methods. 2012 Oct;9(10):999-1003
pubmed: 22941365
Nat Methods. 2017 Jul;14(7):673-678
pubmed: 28604723
Science. 2018 Feb 9;359(6376):
pubmed: 29348367
J Mol Graph. 1996 Feb;14(1):33-8, 27-8
pubmed: 8744570
Nat Genet. 2011 Oct 16;43(11):1059-65
pubmed: 22001755
Cell. 2014 Dec 18;159(7):1665-80
pubmed: 25497547
Cell Syst. 2016 Jul;3(1):95-8
pubmed: 27467249
Nat Rev Genet. 2016 Oct 14;17(11):661-678
pubmed: 27739532
Fold Des. 1997;2(3):173-81
pubmed: 9218955
Cell Rep. 2016 May 31;15(9):2038-49
pubmed: 27210764
PLoS Comput Biol. 2011 Jul;7(7):e1002125
pubmed: 21779160
Nat Rev Genet. 2013 Jun;14(6):390-403
pubmed: 23657480
Cell. 2015 May 21;161(5):1012-1025
pubmed: 25959774
Proc Natl Acad Sci U S A. 2015 Nov 24;112(47):E6456-65
pubmed: 26499245
EMBO J. 2017 Dec 15;36(24):3573-3599
pubmed: 29217591
Cell. 2017 May 4;169(4):693-707.e14
pubmed: 28475897
Nat Genet. 2018 May;50(5):662-667
pubmed: 29662163
Nat Struct Mol Biol. 2017 Feb;24(2):99-107
pubmed: 28067915
Nucleus. 2017 Jul 4;8(4):353-359
pubmed: 28406741
Soft Matter. 2018 Feb 14;14(7):1171-1180
pubmed: 29349466
Bioinformatics. 2012 Dec 1;28(23):3131-3
pubmed: 23023982
Nucleic Acids Res. 2017 Apr 20;45(7):3663-3673
pubmed: 28334818
Mol Cell. 2016 Jun 2;62(5):668-80
pubmed: 27259200
Genome Biol. 2016 Oct 19;17(1):215
pubmed: 27760553
Cell. 2017 Oct 19;171(3):557-572.e24
pubmed: 29053968
J Cell Biol. 2019 May 6;218(5):1511-1530
pubmed: 30824489