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Cairns J, Lynch AG, Tavaré S. Quantifying the impact of inter-site heterogeneity on the distribution of ChIP-seq data. Front Genet. 2014;5:399doi: 10.3389/fgene.2014.00399.
Cairns, J., Lynch, A. G., & Tavaré, S. (2014). Quantifying the impact of inter-site heterogeneity on the distribution of ChIP-seq data. Frontiers in genetics, 5399. https://doi.org/10.3389/fgene.2014.00399
Cairns, Jonathan, et al. "Quantifying the impact of inter-site heterogeneity on the distribution of ChIP-seq data." Frontiers in genetics vol. 5 (2014): 399. doi: https://doi.org/10.3389/fgene.2014.00399
Cairns J, Lynch AG, Tavaré S. Quantifying the impact of inter-site heterogeneity on the distribution of ChIP-seq data. Front Genet. 2014 Nov 14;5:399. doi: 10.3389/fgene.2014.00399. eCollection 2014. PMID: 25452765; PMCID: PMC4231950.
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Front Genet. 2014 Nov 14;5:399. doi: 10.3389/fgene.2014.00399. eCollection 2014.

Quantifying the impact of inter-site heterogeneity on the distribution of ChIP-seq data.

Frontiers in genetics

Jonathan Cairns, Andy G Lynch, Simon Tavaré

Affiliations

  1. Nuclear Dynamics Group, The Babraham Institute Cambridge, UK ; Cancer Research UK Cambridge Institute, University of Cambridge Cambridge, UK.
  2. Cancer Research UK Cambridge Institute, University of Cambridge Cambridge, UK.

PMID: 25452765 PMCID: PMC4231950 DOI: 10.3389/fgene.2014.00399

Abstract

Chromatin Immunoprecipitation followed by sequencing (ChIP-seq) is a valuable tool for epigenetic studies. Analysis of the data arising from ChIP-seq experiments often requires implicit or explicit statistical modeling of the read counts. The simple Poisson model is attractive, but does not provide a good fit to observed ChIP-seq data. Researchers therefore often either extend to a more general model (e.g., the Negative Binomial), and/or exclude regions of the genome that do not conform to the model. Since many modeling strategies employed for ChIP-seq data reduce to fitting a mixture of Poisson distributions, we explore the problem of inferring the optimal mixing distribution. We apply the Constrained Newton Method (CNM), which suggests the Negative Binomial - Negative Binomial (NB-NB) mixture model as a candidate for modeling ChIP-seq data. We illustrate fitting the NB-NB model with an accelerated EM algorithm on four data sets from three species. Zero-inflated models have been suggested as an approach to improve model fit for ChIP-seq data. We show that the NB-NB mixture model requires no zero-inflation and suggest that in some cases the need for zero inflation is driven by the model's inability to cope with both artifactual large read counts and the frequently observed very low read counts. We see that the CNM-based approach is a useful diagnostic for the assessment of model fit and inference in ChIP-seq data and beyond. Use of the suggested NB-NB mixture model will be of value not only when calling peaks or otherwise modeling ChIP-seq data, but also when simulating data or constructing blacklists de novo.

Keywords: ChIP-seq; Negative Binomial; high-throughput sequencing; mixture model; zero-inflation

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