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A Wavelet Approach to Detect Enriched Regions and Explore Epigenomic Landscapes
Author(s) -
Nha Nguyen,
An Vo,
KyoungJae Won
Publication year - 2014
Publication title -
journal of computational biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.585
H-Index - 95
eISSN - 1557-8666
pISSN - 1066-5277
DOI - 10.1089/cmb.2014.0095
Subject(s) - zoom , epigenomics , computational biology , histone , wavelet , computer science , domain (mathematical analysis) , epigenetics , pattern recognition (psychology) , artificial intelligence , biology , segmentation , gene , genetics , dna methylation , mathematics , gene expression , paleontology , mathematical analysis , lens (geology)
Epigenetic landscapes represent how cells regulate gene activity. To understand their effect on gene regulation, it is important to detect their occupancy in the genome. Unlike transcription factors whose binding regions are limited to narrow regions, histone modification marks are enriched over broader areas. The stochastic characteristics unique to each mark make it hard to detect their enrichment. Classically, a predefined window has been used to detect their enrichment. However, these approaches heavily rely on the predetermined parameters. Also, the window-based approaches cannot handle the enrichment of multiple marks. We propose a novel algorithm, called SeqW, to detect enrichment of multiple histone modification marks. SeqW applies a zooming approach to detect a broadly enriched domain. The zooming approach helps domain detection by increasing signal-to-noise ratio. The borders of the domains are detected by studying the characteristics of signals in the wavelet domain. We show that SeqW outperformed previous predictors in detecting broad peaks. Also, we applied SeqW in studying spatial combinations of histone modification patterns.

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