High-Throughput Computational Approaches to Analyzing Histone Modification Next-Generation Sequencing Data  

Jie Lv1 , Hongbo Liu1 , Qiong Wu1 , Yan Zhang2
1 School of Life Science and Technology, State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin, 150001, China
2 College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
Author    Correspondence author
Computational Molecular Biology, 2012, Vol. 2, No. 2   doi: 10.5376/cmb.2012.02.0002
Received: 19 Mar., 2012    Accepted: 19 Jun., 2012    Published: 02 Jul., 2012
© 2012 BioPublisher Publishing Platform
This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Preferred citation for this article:

Lv et al., 2012, High-Throughput Computational Approaches to Analyzing Histone Modification Next-Generation Sequencing Data, Computational Molecular Biology, Vol.2, No.2 1-13 (doi: 10.5376/cmb.2012.02.0002)


Chromatin immunoprecipitation followed by sequencing (ChIP–seq) facilitates systematic analysis of chemical modifications of histone tails. As the cost of next-generation sequencing continues to drop, genome-wide histone modification sequencing becomes a common approach in a variety of researches in the epigenetic area. However, challenges of efficient ChIP–seq data analysis are now the main hurdle to interpret the histone modification ChIP-seq data, calling for continued enhancements of computational approaches. Here we provide a pragmatic overview of available computational approaches for the study of histone modification ChIP-seq data. We present the latest advances of computational methods for systematically detecting and functionally characterizing various types of histone modification ChIP-seq data, discuss the software packages currently available for performing tasks from short read mapping, peak calling to downstream genomic characterization and genome-wide visualization. We also present that the regulatory roles of histone modifications upon gene expression can be inferred by developing algorithms and methods specifically for histone modification ChIP-seq data. Such approaches will facilitate the epigenetic regulatory network construction and provide explicit biological hypothesis for further experiment testing. We also describe some challenges and important directions for histone modification analysis based on ChIP-seq data in future. We envision that the advances of computational approaches will bring more about a bright future for large-scale histone modification studies.

Next-generation sequencing; Histone modification; Computational approaches; Peak calling; ChIP sequencing
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