MUSIC: identification of enriched regions in ChIP-Seq experiments using a mappability-corrected multiscale signal processing framework

Harmanci, Arif; Rozowsky, Joel; Gerstein, Mark B.

doi:10.1186/s13059-014-0474-3

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MUSIC: identification of enriched regions in ChIP-Seq experiments using a mappability-corrected multiscale signal processing framework

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Author(s): Arif Harmanci , Joel Rozowsky , Mark Gerstein

Publication date (Electronic): 8 October 2014

Journal: Genome Biology

Publisher: BioMed Central

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Abstract

We present MUSIC, a signal processing approach for identification of enriched regions in ChIP-Seq data, available at music.gersteinlab.org. MUSIC first filters the ChIP-Seq read-depth signal for systematic noise from non-uniform mappability, which fragments enriched regions. Then it performs a multiscale decomposition, using median filtering, identifying enriched regions at multiple length scales. This is useful given the wide range of scales probed in ChIP-Seq assays. MUSIC performs favorably in terms of accuracy and reproducibility compared with other methods. In particular, analysis of RNA polymerase II data reveals a clear distinction between the stalled and elongating forms of the polymerase.

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The online version of this article (doi:10.1186/s13059-014-0474-3) contains supplementary material, which is available to authorized users.

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A clustering approach for identification of enriched domains from histone modification ChIP-Seq data.

Chongzhi Zang, Dustin E. Schones, Chen Zeng … (2009)

Chromatin states are the key to gene regulation and cell identity. Chromatin immunoprecipitation (ChIP) coupled with high-throughput sequencing (ChIP-Seq) is increasingly being used to map epigenetic states across genomes of diverse species. Chromatin modification profiles are frequently noisy and diffuse, spanning regions ranging from several nucleosomes to large domains of multiple genes. Much of the early work on the identification of ChIP-enriched regions for ChIP-Seq data has focused on identifying localized regions, such as transcription factor binding sites. Bioinformatic tools to identify diffuse domains of ChIP-enriched regions have been lacking. Based on the biological observation that histone modifications tend to cluster to form domains, we present a method that identifies spatial clusters of signals unlikely to appear by chance. This method pools together enrichment information from neighboring nucleosomes to increase sensitivity and specificity. By using genomic-scale analysis, as well as the examination of loci with validated epigenetic states, we demonstrate that this method outperforms existing methods in the identification of ChIP-enriched signals for histone modification profiles. We demonstrate the application of this unbiased method in important issues in ChIP-Seq data analysis, such as data normalization for quantitative comparison of levels of epigenetic modifications across cell types and growth conditions. http://home.gwu.edu/ approximately wpeng/Software.htm. Supplementary data are available at Bioinformatics online.

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Design and analysis of ChIP-seq experiments for DNA-binding proteins

Peter V. Kharchenko, Michael Tolstorukov, Peter Park (2008)

Recent progress in massively parallel sequencing platforms has allowed for genome-wide measurements of DNA-associated proteins using a combination of chromatin immunoprecipitation and sequencing (ChIP-seq). While a variety of methods exist for analysis of the established microarray alternative (ChIP-chip), few approaches have been described for processing ChIP-seq data. To fill this gap, we propose an analysis pipeline specifically designed to detect protein binding positions with high accuracy. Using three separate datasets, we illustrate new methods for improving tag alignment and correcting for background signals. We also compare sensitivity and spatial precision of several novel and previously described binding detection algorithms. Finally, we analyze the relationship between the depth of sequencing and characteristics of the detected binding positions, and provide a method for estimating the sequencing depth necessary for a desired coverage of protein binding sites.

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Chromatin organization is a major influence on regional mutation rates in human cancer cells.

Benjamin Schuster-Böckler, Ben Lehner (2012)

Cancer genome sequencing provides the first direct information on how mutation rates vary across the human genome in somatic cells. Testing diverse genetic and epigenetic features, here we show that mutation rates in cancer genomes are strikingly related to chromatin organization. Indeed, at the megabase scale, a single feature—levels of the heterochromatin-associated histone modification H3K9me3—can account for more than 40% of mutation-rate variation, and a combination of features can account for more than 55%. The strong association between mutation rates and chromatin organization is upheld in samples from different tissues and for different mutation types. This suggests that the arrangement of the genome into heterochromatin- and euchromatin-like domains is a dominant influence on regional mutation-rate variation in human somatic cells.

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Author and article information

Contributors

Arif Harmanci: arif.harmanci@yale.edu

Joel Rozowsky: joel.rozowsky@yale.edu

Mark Gerstein: mark.gerstein@yale.edu

Journal

Journal ID (nlm-ta): Genome Biol

Title: Genome Biology

Publisher: BioMed Central (London )

ISSN (Print): 1465-6906

ISSN (Electronic): 1465-6914

Publication date (Electronic): 8 October 2014

Publication date PMC-release: 8 October 2014

Publication date (Print): 2014

Volume: 15

Issue: 10

Electronic Location Identifier: 474

Affiliations

[ ]Program in Computational Biology and Bioinformatics, Yale University, 260 Whitney Avenue, New Haven, CT 06520 USA

[ ]Department of Molecular Biophysics and Biochemistry, Yale University, 260 Whitney Avenue, New Haven, CT 06520 USA

[ ]Department of Computer Science, Yale University, 260 Whitney Avenue, New Haven, CT 06520 USA

Article

Publisher ID: 474

DOI: 10.1186/s13059-014-0474-3

PMC ID: 4234855

PubMed ID: 25292436

SO-VID: cea05f29-b5ad-4b0d-bea3-264d43e35e25

License:

This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

History

Date received : 25 June 2014

Date accepted : 15 September 2014

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ScienceOpen disciplines: Genetics

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MUSIC: identification of enriched regions in ChIP-Seq experiments using a mappability-corrected multiscale signal processing framework

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Most cited references 28

A clustering approach for identification of enriched domains from histone modification ChIP-Seq data.

Design and analysis of ChIP-seq experiments for DNA-binding proteins

Chromatin organization is a major influence on regional mutation rates in human cancer cells.

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