Literature DB >> 22130888

Identifying differential histone modification sites from ChIP-seq data.

Han Xu1, Wing-Kin Sung.   

Abstract

Epigenetic modifications are critical to gene regulations and genome functions. Among different epigenetic modifications, it is of great interest to study the differential histone modification sites (DHMSs), which contribute to the epigenetic dynamics and the gene regulations among various cell-types or environmental responses. ChIP-seq is a robust and comprehensive approach to capture the histone modifications at the whole genome scale. By comparing two histone modification ChIP-seq libraries, the DHMSs are potentially identifiable. With this aim, we proposed an approach called ChIPDiff for the genome-wide comparison of histone modification sites identified by ChIP-seq (Xu, Wei, Lin et al., Bioinformatics 24:2344-2349, 2008). The 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. The result showed significant correlation between the histone modification states and the gene expression levels.

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Year:  2012        PMID: 22130888     DOI: 10.1007/978-1-61779-400-1_19

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  6 in total

1.  Computational inference of mRNA stability from histone modification and transcriptome profiles.

Authors:  Chengyang Wang; Rui Tian; Qian Zhao; Han Xu; Clifford A Meyer; Cheng Li; Yong Zhang; X Shirley Liu
Journal:  Nucleic Acids Res       Date:  2012-04-10       Impact factor: 16.971

2.  dCLIP: a computational approach for comparative CLIP-seq analyses.

Authors:  Tao Wang; Yang Xie; Guanghua Xiao
Journal:  Genome Biol       Date:  2014-01-07       Impact factor: 13.583

Review 3.  From reads to insight: a hitchhiker's guide to ATAC-seq data analysis.

Authors:  Feng Yan; David R Powell; David J Curtis; Nicholas C Wong
Journal:  Genome Biol       Date:  2020-02-03       Impact factor: 13.583

4.  De novo ChIP-seq analysis.

Authors:  Xin He; A Ercument Cicek; Yuhao Wang; Marcel H Schulz; Hai-Son Le; Ziv Bar-Joseph
Journal:  Genome Biol       Date:  2015-09-23       Impact factor: 13.583

5.  Epigenetic reprogramming in Mist1(-/-) mice predicts the molecular response to cerulein-induced pancreatitis.

Authors:  Rashid Mehmood; Gabor Varga; Sonali Q Mohanty; Scott W Laing; Yuefeng Lu; Charis L Johnson; Alexei Kharitonenkov; Christopher L Pin
Journal:  PLoS One       Date:  2014-01-21       Impact factor: 3.240

6.  Epigenetic regulation of gene expression in cancer: techniques, resources and analysis.

Authors:  Luciane T Kagohara; Genevieve L Stein-O'Brien; Dylan Kelley; Emily Flam; Heather C Wick; Ludmila V Danilova; Hariharan Easwaran; Alexander V Favorov; Jiang Qian; Daria A Gaykalova; Elana J Fertig
Journal:  Brief Funct Genomics       Date:  2018-01-01       Impact factor: 4.241

  6 in total

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