Bioinformatics Advance Access published online on July 29, 2008
Bioinformatics, doi:10.1093/bioinformatics/btn402
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An HMM approach to genome-wide identification of differential histone modification sites from ChIP-seq data
1Computational & Mathematical Biology Group, Genome Institute of Singapore, 138672, Sin-gapore; 2Genome Technology & Biology Group, Genome Institute of Singapore, 138672, Sin-gapore; 3School of Computer Engineering, Nanyang Technological University, 637553, Singa-pore; 4School of Computing, National University of Singapore, 117543, Singapore
*To whom correspondence should be addressed. Wing-Kin Sung, Feng Lin, E-mail: sungk{at}gis.a-star.edu.sg, asflin{at}ntu.edu.sg
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Abstract |
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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 environ-mental 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 com-paring 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.
Availability: http://cmb.gis.a-star.edu.sg/ChIPSeq/tools.htm
Contact: sungk{at}gis.a-star.edu.sg, asflin{at}ntu.edu.sg
Supplementary Information:Supplementary methods and data are available at Bioinformatics online.
Associate Editor: Dr. Trey Ideker
Received on April 9, 2008; revised on June 13, 2008; accepted on July 28, 2008
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