MAPS: Model-based analysis of long-range chromatin interactions from PLAC-seq and HiChIP experiments.


Journal

PLoS computational biology
ISSN: 1553-7358
Titre abrégé: PLoS Comput Biol
Pays: United States
ID NLM: 101238922

Informations de publication

Date de publication:
04 2019
Historique:
received: 25 09 2018
accepted: 28 03 2019
revised: 25 04 2019
pubmed: 16 4 2019
medline: 22 5 2019
entrez: 16 4 2019
Statut: epublish

Résumé

Hi-C and chromatin immunoprecipitation (ChIP) have been combined to identify long-range chromatin interactions genome-wide at reduced cost and enhanced resolution, but extracting information from the resulting datasets has been challenging. Here we describe a computational method, MAPS, Model-based Analysis of PLAC-seq and HiChIP, to process the data from such experiments and identify long-range chromatin interactions. MAPS adopts a zero-truncated Poisson regression framework to explicitly remove systematic biases in the PLAC-seq and HiChIP datasets, and then uses the normalized chromatin contact frequencies to identify significant chromatin interactions anchored at genomic regions bound by the protein of interest. MAPS shows superior performance over existing software tools in the analysis of chromatin interactions from multiple PLAC-seq and HiChIP datasets centered on different transcriptional factors and histone marks. MAPS is freely available at https://github.com/ijuric/MAPS.

Identifiants

pubmed: 30986246
doi: 10.1371/journal.pcbi.1006982
pii: PCOMPBIOL-D-18-01649
pmc: PMC6483256
doi:

Substances chimiques

Chromatin 0

Types de publication

Journal Article Research Support, N.I.H., Extramural

Langues

eng

Sous-ensembles de citation

IM

Pagination

e1006982

Subventions

Organisme : NIDDK NIH HHS
ID : U54 DK107977
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01 HL129132
Pays : United States

Déclaration de conflit d'intérêts

The authors have declared that no competing interests exist.

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Auteurs

Ivan Juric (I)

Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, OH, United States of America.

Miao Yu (M)

Ludwig Institute for Cancer Research, La Jolla, CA, United States of America.

Armen Abnousi (A)

Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, OH, United States of America.

Ramya Raviram (R)

Ludwig Institute for Cancer Research, La Jolla, CA, United States of America.

Rongxin Fang (R)

Ludwig Institute for Cancer Research, La Jolla, CA, United States of America.
Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA, United States of America.

Yuan Zhao (Y)

Ludwig Institute for Cancer Research, La Jolla, CA, United States of America.
Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA, United States of America.

Yanxiao Zhang (Y)

Ludwig Institute for Cancer Research, La Jolla, CA, United States of America.

Yunjiang Qiu (Y)

Ludwig Institute for Cancer Research, La Jolla, CA, United States of America.
Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA, United States of America.

Yuchen Yang (Y)

Departments of Genetics, Biostatistics, and Computer Science, University of North Carolina, Chapel Hill, NC, United States of America.

Yun Li (Y)

Departments of Genetics, Biostatistics, and Computer Science, University of North Carolina, Chapel Hill, NC, United States of America.

Bing Ren (B)

Ludwig Institute for Cancer Research, La Jolla, CA, United States of America.
Department of Cellular and Molecular Medicine, Center for Epigenomics, University of California, San Diego School of Medicine, La Jolla, CA, United States of America.

Ming Hu (M)

Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, OH, United States of America.

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Classifications MeSH