Journal Article

An HMM approach to genome-wide identification of differential histone modification sites from ChIP-seq data

Han Xu, Chia-Lin Wei, Feng Lin and Wing-Kin Sung

in Bioinformatics

Volume 24, issue 20, pages 2344-2349
Published in print October 2008 | ISSN: 1367-4803
Published online July 2008 | e-ISSN: 1460-2059 | DOI:
An HMM approach to genome-wide identification of differential histone modification sites from ChIP-seq data

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Motivation: Epigenetic modifications are one of the critical factors to regulate gene expression and genome function. Among different epigenetic modifications, the differential histone modification sites (DHMSs) are of great interest to study the dynamic nature of epigenetic and gene expression regulations among various cell types, stages or environmental responses. To capture the histone modifications at whole genome scale, ChIP-seq technology is becoming a robust and comprehensive approach. Thus the DHMSs are potentially identifiable by comparing two ChIP-seq libraries. However, little has been addressed on this issue in literature.

Results: Aiming at identifying DHMSs, we propose an approach called ChIPDiff for the genome-wide comparison of histone modification sites identified by ChIP-seq. Based on the observations of ChIP fragment counts, the proposed approach employs a hidden Markov model (HMM) to infer the states of histone modification changes at each genomic location. We evaluated the performance of ChIPDiff by comparing the H3K27me3 modification sites between mouse embryonic stem cell (ESC) and neural progenitor cell (NPC). We demonstrated that the H3K27me3 DHMSs identified by our approach are of high sensitivity, specificity and technical reproducibility. ChIPDiff was further applied to uncover the differential H3K4me3 and H3K36me3 sites between different cell states. Interesting biological discoveries were achieved from such comparison in our study.



Supplementary information: Supplementary methods and data are available at Bioinformatics online.

Journal Article.  4113 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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