ChromaSig: A probabilistic approach to finding common chromatin signatures in the human genome

Gary Hon, Bing Ren, Wei Wang

Research output: Contribution to journalArticle

104 Citations (Scopus)

Abstract

Computational methods to identify functional genomic elements using genetic information have been very successful in determining gene structure and in identifying a handful of cis-regulatory elements. But the vast majority of regulatory elements have yet to be discovered, and it has become increasingly apparent that their discovery will not come from using genetic information alone. Recently, high-throughput technologies have enabled the creation of information-rich epigenetic maps, most notably for histone modifications. However, tools that search for functional elements using this epigenetic information have been lacking. Here, we describe an unsupervised learning method called ChromaSig to find, in an unbiased fashion, commonly occurring chromatin signatures in both tiling microarray and sequencing data. Applying this algorithm to nine chromatin marks across a 1% sampling of the human genome in HeLa cells, we recover eight clusters of distinct chromatin signatures, five of which correspond to known patterns associated with transcriptional promoters and enhancers. Interestingly, we observe that the distinct chromatin signatures found at enhancers mark distinct functional classes of enhancers in terms of transcription factor and coactivator binding. In addition, we identify three clusters of novel chromatin signatures that contain evolutionarily conserved sequences and potential cis-regulatory elements. Applying ChromaSig to a panel of 21 chromatin marks mapped genomewide by ChIP-Seq reveals 16 classes of genomic elements marked by distinct chromatin signatures. Interestingly, four classes containing enrichment for repressive histone modifications appear to be locally heterochromatic sites and are enriched in quickly evolving regions of the genome. The utility of this approach in uncovering novel, functionally significant genomic elements will aid future efforts of genome annotation via chromatin modifications.

Original languageEnglish (US)
Article numbere1000201
JournalPLoS Computational Biology
Volume4
Issue number10
DOIs
StatePublished - Oct 1 2008

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Chromatin
Probabilistic Approach
Human Genome
chromatin
genomics
Genome
Signature
genome
Genes
Histone Code
Distinct
regulatory sequences
Unsupervised learning
Transcription factors
Microarrays
Computational methods
learning
Epigenomics
histones
epigenetics

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Modeling and Simulation
  • Ecology
  • Molecular Biology
  • Genetics
  • Cellular and Molecular Neuroscience
  • Computational Theory and Mathematics

Cite this

ChromaSig : A probabilistic approach to finding common chromatin signatures in the human genome. / Hon, Gary; Ren, Bing; Wang, Wei.

In: PLoS Computational Biology, Vol. 4, No. 10, e1000201, 01.10.2008.

Research output: Contribution to journalArticle

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