![]() The required depth depends mainly on the size of the genome and the number and size of the binding sites of the protein. Įffective analysis of ChIP-seq data requires sufficient coverage by sequence reads (sequencing depth). ChIP-seq is now the most widely used procedure for genome-wide assays of protein-DNA interaction, and its use in mapping histone modifications has been seminal in epigenetics research. The use of NGS provides relatively high resolution, low noise, and high genomic coverage compared with ChIP-chip assays (ChIP followed by microarray hybridization). Computational mapping of the sequenced DNA identifies the genomic locations of bound DNA-binding enzymes, modified histones, chaperones, nucleosomes, and transcription factors (TFs), thereby illuminating the role of these protein-DNA interactions in gene expression and other cellular processes. ChIP-seq first cross-links bound proteins to chromatin, fragments the chromatin, captures the DNA fragments bound to one protein using an antibody specific to it, and sequences the ends of the captured fragments using next-generation sequencing (NGS). The funders had no role in the preparation of the manuscript.Ĭompeting interests: The authors have declared that no competing interests exist.Ĭhromatin immunoprecipitation followed by sequencing (ChIP-seq), first described in 2007 –, allows in vivo determination of where a protein binds the genome, which can be transcription factors, DNA-binding enzymes, histones, chaperones, or nucleosomes. IL gratefully acknowledges support from UNL IANR. QL is partially supported by National Institutes of Health grants 1UL1 RR033184-01 and R01 GM109453. PM was supported by the EU Marie Curie Initial Training Network SYSFLO (agreement number 237909). This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.įunding: TLB is funded by National Institutes of Health grant R0-1 RR021692-05. PLoS Comput Biol 9(11):Įditor: Fran Lewitter, Whitehead Institute, United States of AmericaĬopyright: © 2013 Bailey et al. (2013) Practical Guidelines for the Comprehensive Analysis of ChIP-seq Data. ![]() Each step in the workflow is described in detail in the following sections.Ĭitation: Bailey T, Krajewski P, Ladunga I, Lefebvre C, Li Q, Liu T, et al. 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. We also highlight the challenges and problems associated with each step in ChIP-seq data analysis. At each step in our guidelines we discuss some of the software tools most frequently used. 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. Here, we present step-by-step guidelines for the computational analysis of ChIP-seq data. 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. To this end, chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) is the standard methodology. 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.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |