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Design and analysis of ChIP-seq experiments for DNA-binding proteins.

ABSTRACT: Recent progress in massively parallel sequencing platforms has enabled genome-wide characterization of DNA-associated proteins using the combination of chromatin immunoprecipitation and sequencing (ChIP-seq). Although a variety of methods exist for analysis of the established alternative ChIP microarray (ChIP-chip), few approaches have been described for processing ChIP-seq data. To fill this gap, we propose an analysis pipeline specifically designed to detect protein-binding positions with high accuracy. Using previously reported data sets for three transcription factors, we illustrate methods for improving tag alignment and correcting for background signals. We compare the sensitivity and spatial precision of three peak detection algorithms with published methods, demonstrating gains in spatial precision when an asymmetric distribution of tags on positive and negative strands is considered. We also analyze the relationship between the depth of sequencing and characteristics of the detected binding positions, and provide a method for estimating the sequencing depth necessary for a desired coverage of protein binding sites.

SUBMITTER: Kharchenko PV

PROVIDER: S-EPMC2597701 | biostudies-literature | 2008 Dec

REPOSITORIES: biostudies-literature

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Design and analysis of ChIP-seq experiments for DNA-binding proteins.

Kharchenko Peter V PV Tolstorukov Michael Y MY Park Peter J PJ

Nature biotechnology 20081116 12

Recent progress in massively parallel sequencing platforms has enabled genome-wide characterization of DNA-associated proteins using the combination of chromatin immunoprecipitation and sequencing (ChIP-seq). Although a variety of methods exist for analysis of the established alternative ChIP microarray (ChIP-chip), few approaches have been described for processing ChIP-seq data. To fill this gap, we propose an analysis pipeline specifically designed to detect protein-binding positions with high ...[more]

PMID: 19029915

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Project description:BackgroundMotif enrichment analysis of transcription factor ChIP-seq data can help identify transcription factors that cooperate or compete. Previously, little attention has been given to comparative motif enrichment analysis of pairs of ChIP-seq experiments, where the binding of the same transcription factor is assayed under different conditions. Such comparative analysis could potentially identify the distinct regulatory partners/competitors of the assayed transcription factor under different conditions or at different stages of development.ResultsWe describe a new methodology for identifying sequence motifs that are differentially enriched in one set of DNA or RNA sequences relative to another set, and apply it to paired ChIP-seq experiments. We show that, using paired ChIP-seq data for a single transcription factor, differential motif enrichment analysis identifies all the known key transcription factors involved in the transformation of non-cancerous immortalized breast cells (MCF10A-ER-Src cells) into cancer stem cells whereas non-differential motif enrichment analysis does not. We also show that differential motif enrichment analysis identifies regulatory motifs that are significantly enriched at constrained locations within the bound promoters, and that these motifs are not identified by non-differential motif enrichment analysis. Our methodology differs from other approaches in that it leverages both comparative enrichment and positional enrichment of motifs in ChIP-seq peak regions or in the promoters of genes bound by the transcription factor.ConclusionsWe show that differential motif enrichment analysis of paired ChIP-seq experiments offers biological insights not available from non-differential analysis. In contrast to previous approaches, our method detects motifs that are enriched in a constrained region in one set of sequences, but not enriched in the same region in the comparative set. We have enhanced the web-based CentriMo algorithm to allow it to perform the constrained differential motif enrichment analysis described in this paper, and CentriMo's on-line interface (http://meme.ebi.edu.au) provides dozens of databases of DNA- and RNA-binding motifs from a full range of organisms. All data and output files presented here are available at http://research.imb.uq.edu.au/t.bailey/supplementary\_data/Lesluyes2014.

Dataset Information

Design and analysis of ChIP-seq experiments for DNA-binding proteins.

Publications

Design and analysis of ChIP-seq experiments for DNA-binding proteins.

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