Practical Guidelines for the Comprehensive Analysis of ChIP-seq Data

Bailey, Timothy, Krajewski, Pawel, Ladunga, Istvan, Lefebvre, Celine, Li,Qunhua, Liu, Tao, Madrigal, Pedro, Taslim, Cenny and Zhang, Jie (2013) Practical Guidelines for the Comprehensive Analysis of ChIP-seq Data. PLoS Computational Biology, 9 11: e1003326.1-e1003326.7. doi:10.1371/journal.pcbi.1003326

Author Bailey, Timothy
Krajewski, Pawel
Ladunga, Istvan
Lefebvre, Celine
Liu, Tao
Madrigal, Pedro
Taslim, Cenny
Zhang, Jie
Title Practical Guidelines for the Comprehensive Analysis of ChIP-seq Data
Journal name PLoS Computational Biology   Check publisher's open access policy
ISSN 1553-734X
Publication date 2013-01-01
Year available 2013
Sub-type Article (original research)
DOI 10.1371/journal.pcbi.1003326
Open Access Status DOI
Volume 9
Issue 11
Start page e1003326.1
End page e1003326.7
Total pages 8
Place of publication San Francisco, CA United States
Publisher Public Library of Science
Language eng
Subject 2804 Cellular and Molecular Neuroscience
2303 Ecology
1312 Molecular Biology
1311 Genetics
1105 Dentistry
2611 Modelling and Simulation
1703 Computational Theory and Mathematics
Abstract Mapping the chromosomal locations of transcription factors, nucleosomes, histone modifications, chromatin remodeling enzymes, chaperones, and polymerases is one of the key tasks of modern biology, as evidenced by the Encyclopedia of DNA Elements (ENCODE) Project. To this end, chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) is the standard methodology. Mapping such protein-DNA interactions in vivo using ChIP-seq presents multiple challenges not only in sample preparation and sequencing but also for computational analysis. Here, we present step-by-step guidelines for the computational analysis of ChIP-seq data. We address all the major steps in the analysis of ChIP-seq data: sequencing depth selection, quality checking, mapping, data normalization, assessment of reproducibility, peak calling, differential binding analysis, controlling the false discovery rate, peak annotation, visualization, and motif analysis. At each step in our guidelines we discuss some of the software tools most frequently used. We also highlight the challenges and problems associated with each step in ChIP-seq data analysis. We present a concise workflow for the analysis of ChIP-seq data in Figure 1 that complements and expands on the recommendations of the ENCODE and modENCODE projects. Each step in the workflow is described in detail in the following sections.
Q-Index Code C1
Q-Index Status Confirmed Code
Grant ID R0-1 RR021692-05
1UL1 RR033184-01
R01 GM109453
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2014 Collection
Institute for Molecular Bioscience - Publications
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