A physical basis for quantitative ChIP-sequencing.
ChIP normalization
ChIP-Seq
ChIP-sequencing
antibody specificity
biophysics
chromatin immunoprecipitation (ChiP)
epigenetics
mathematical modeling
quantitative ChIP
quantitative ChIP-Seq
spike-in
Journal
The Journal of biological chemistry
ISSN: 1083-351X
Titre abrégé: J Biol Chem
Pays: United States
ID NLM: 2985121R
Informations de publication
Date de publication:
20 11 2020
20 11 2020
Historique:
received:
22
07
2020
revised:
09
09
2020
pubmed:
1
10
2020
medline:
10
3
2021
entrez:
30
9
2020
Statut:
ppublish
Résumé
ChIP followed by next-generation sequencing (ChIP-Seq) is a key technique for mapping the distribution of histone posttranslational modifications (PTMs) and chromatin-associated factors across genomes. There is a perceived challenge to define a quantitative scale for ChIP-Seq data, and as such, several approaches making use of exogenous additives, or "spike-ins," have recently been developed. Herein, we report on the development of a quantitative, physical model defining ChIP-Seq. The quantitative scale on which ChIP-Seq results should be compared emerges from the model. To test the model and demonstrate the quantitative scale, we examine the impacts of an EZH2 inhibitor through the lens of ChIP-Seq. We report a significant increase in immunoprecipitation of presumed off-target histone PTMs after inhibitor treatment, a trend predicted by the model but contrary to spike-in-based indications. Our work also identifies a sensitivity issue in spike-in normalization that has not been considered in the literature, placing limitations on its utility and trustworthiness. We call our new approach the
Identifiants
pubmed: 32994221
pii: S0021-9258(17)50412-3
doi: 10.1074/jbc.RA120.015353
pmc: PMC7681007
pii:
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
15826-15837Subventions
Organisme : NCI NIH HHS
ID : F32 CA225043
Pays : United States
Organisme : NCI NIH HHS
ID : F99 CA245821
Pays : United States
Organisme : NIGMS NIH HHS
ID : R35 GM124736
Pays : United States
Commentaires et corrections
Type : CommentIn
Informations de copyright
© 2020 Dickson et al.
Déclaration de conflit d'intérêts
Conflict of interest—The authors declare that they have no conflicts of interest with the contents of this article.
Références
Front Genet. 2014 Apr 10;5:75
pubmed: 24782889
Mol Cell. 2015 Aug 6;59(3):502-11
pubmed: 26212453
Mol Cell. 2018 Oct 4;72(1):162-177.e7
pubmed: 30244833
Nat Methods. 2019 Apr;16(4):289-292
pubmed: 30923383
Nat Protoc. 2019 Dec;14(12):3275-3302
pubmed: 31723301
Proc Natl Acad Sci U S A. 1985 Oct;82(19):6470-4
pubmed: 2995966
J Mol Biol. 2012 Dec 14;424(5):391-9
pubmed: 23041298
Nat Commun. 2019 Apr 29;10(1):1930
pubmed: 31036827
Science. 2007 Jun 8;316(5830):1497-502
pubmed: 17540862
Angew Chem Int Ed Engl. 1998 Nov 2;37(20):2754-2794
pubmed: 29711117
Bioinformatics. 2010 Mar 15;26(6):841-2
pubmed: 20110278
BMC Genomics. 2013;14 Suppl 8:S3
pubmed: 24564479
Mol Cell Biol. 2015 Dec 28;36(5):662-7
pubmed: 26711261
Proc Natl Acad Sci U S A. 2013 May 7;110(19):7922-7
pubmed: 23620515
Genes Dev. 2013 Aug 15;27(16):1787-99
pubmed: 23934658
Mol Cell. 2015 Jun 4;58(5):886-99
pubmed: 26004229
Genome Res. 2009 Feb;19(2):221-33
pubmed: 19047520
Nature. 2012 Sep 6;489(7414):57-74
pubmed: 22955616
Genome Biol. 2008;9(9):R137
pubmed: 18798982
Nat Methods. 2012 Mar 04;9(4):357-9
pubmed: 22388286
Commun Biol. 2018 Dec 5;1:214
pubmed: 30534606
Cancer Cell. 2019 Jan 14;35(1):140-155.e7
pubmed: 30595505
Mol Cell. 2016 Jan 7;61(1):170-80
pubmed: 26687680
Bioinformatics. 2019 Oct 1;35(19):3592-3598
pubmed: 30824903
J Vis Exp. 2017 Aug 1;(126):
pubmed: 28809825
Methods Enzymol. 2016;574:53-77
pubmed: 27423857
Cell Rep. 2014 Nov 6;9(3):1163-70
pubmed: 25437568