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Bioinformatics
Article . 2013 . Peer-reviewed
Data sources: Crossref
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Bioinformatics
Article
Data sources: UnpayWall
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Bioinformatics
Article . 2014
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An integrative approach to understanding the combinatorial histone code at functional elements

Authors: Michael J. Buck; William K. M. Lai;

An integrative approach to understanding the combinatorial histone code at functional elements

Abstract

Abstract Summary: The rapid advancement of genomic technology has revealed the enormous complexity and combinatorial nature of chromatin modifications. To facilitate interpretation of the combinatorial nature of chromatin, we have developed a novel method to integrate all chromatin datasets into distinct nucleosome types (nucleosome alphabet). We have applied this approach to Saccharomyces cerevisiae, generating a nucleosome alphabet, which forms chromatin motifs when mapped back to the genome. By applying novel chromatin alignment and global word search approaches, we have defined distinctive chromatin motifs for introns, origins of replication, tRNAs, antisense transcripts, double-strand-break hotspots and DNase hypersensitive sites, and can distinguish genes by expression level. We have also uncovered strong associations between transcription factor binding and specific types of nucleosomes. Our results demonstrate the uses and functionality of defining a chromatin alphabet and provide a unique and novel framework for exploring chromatin architecture. Contact: mjbuck@buffalo.edu Supplementary Information: Supplementary data are available at Bioinformatics online.

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Keywords

Histone Code, Gene Expression, Genomics, Saccharomyces cerevisiae, Sequence Alignment, Algorithms, Chromatin, Nucleosomes, Transcription Factors

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
7
Average
Average
Top 10%
gold