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Leveraging biological replicates to improve analysis in ChIP-seq experiments.

ABSTRACT: ChIP-seq experiments identify genome-wide profiles of DNA-binding molecules including transcription factors, enzymes and epigenetic marks. Biological replicates are critical for reliable site discovery and are required for the deposition of data in the ENCODE and modENCODE projects. While early reports suggested two replicates were sufficient, the widespread application of the technique has led to emerging consensus that the technique is noisy and that increasing replication may be worthwhile. Additional biological replicates also allow for quantitative assessment of differences between conditions. To date it has remained controversial about how to confirm peak identification and to determine signal strength across biological replicates, particularly when the number of replicates is greater than two. Using objective metrics, we evaluate the consistency of biological replicates in ChIP-seq experiments with more than two replicates. We compare several approaches for binding site determination, including two popular but disparate peak callers, CisGenome and MACS2. Here we propose read coverage as a quantitative measurement of signal strength for estimating sample concordance. Determining binding based on genomic features, such as promoters, is also examined. We find that increasing the number of biological replicates increases the reliability of peak identification. Critically, binding sites with strong biological evidence may be missed if researchers rely on only two biological replicates. When more than two replicates are performed, a simple majority rule (>50% of samples identify a peak) identifies peaks more reliably in all biological replicates than the absolute concordance of peak identification between any two replicates, further demonstrating the utility of increasing replicate numbers in ChIP-seq experiments.

SUBMITTER: Yang Y

PROVIDER: S-EPMC3962196 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Leveraging biological replicates to improve analysis in ChIP-seq experiments.

Yang Yajie Y Fear Justin J Hu Jianhong J Haecker Irina I Zhou Lei L Renne Rolf R Bloom David D McIntyre Lauren M LM

Computational and structural biotechnology journal 20140131

ChIP-seq experiments identify genome-wide profiles of DNA-binding molecules including transcription factors, enzymes and epigenetic marks. Biological replicates are critical for reliable site discovery and are required for the deposition of data in the ENCODE and modENCODE projects. While early reports suggested two replicates were sufficient, the widespread application of the technique has led to emerging consensus that the technique is noisy and that increasing replication may be worthwhile. A ...[more]

PMID: 24688750

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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

Leveraging biological replicates to improve analysis in ChIP-seq experiments.

Publications

Leveraging biological replicates to improve analysis in ChIP-seq experiments.

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