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Joint modeling of ChIP-seq data via a Markov random field model

Bao, Y and Vinciotti, V and Wit, E and Hoen, PAC (2014) 'Joint modeling of ChIP-seq data via a Markov random field model.' Biostatistics, 15 (2). 296 - 310. ISSN 1465-4644

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Abstract

Chromatin ImmunoPrecipitation-sequencing (ChIP-seq) experiments have now become routine in biology for the detection of protein-binding sites. In this paper, we present a Markov random field model for the joint analysis of multiple ChIP-seq experiments. The proposed model naturally accounts for spatial dependencies in the data, by assuming first-order Markov dependence and, for the large proportion of zero counts, by using zero-inflated mixture distributions. In contrast to all other available implementations, the model allows for the joint modeling of multiple experiments, by incorporating key aspects of the experimental design. In particular, the model uses the information about replicates and about the different antibodies used in the experiments. An extensive simulation study shows a lower false non-discovery rate for the proposed method, compared with existing methods, at the same false discovery rate. Finally, we present an analysis on real data for the detection of histone modifications of two chromatin modifiers from eight ChIP-seq experiments, including technical replicates with different IP efficiencies. © 2013 The Author 2013.

Item Type: Article
Subjects: Q Science > QA Mathematics
Q Science > QH Natural history > QH301 Biology
Divisions: Faculty of Science and Health > Mathematical Sciences, Department of
Depositing User: Jim Jamieson
Date Deposited: 04 Dec 2015 13:13
Last Modified: 02 Sep 2019 21:15
URI: http://repository.essex.ac.uk/id/eprint/15590

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