Gaussian derivative wavelets identify dynamic changes in histone modification

Nha Nguyen, Kyoung Jae Won

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Epigenetic landscapes reveal how cells regulate genes in a cell-type or condition specific manner. Genome-wide surveys using histone modification showed cell-type specific regulatory regions. A number of computational methods were designed to identify cell-type specific regulatory regions using epigenome data. Most of them were designed to identify the enrichment of histone modification or their changes. However, they did not consider the shape of epigenetic signals, which represents the condition for protein binding at gene regulatory regions. We present a computational method to detect epigenetic changes using the shape of the signals for histone modification. Employing a Gaussian Derivative Wavelet (CGDWavelet) approach, the proposed method models a nucleosome with a Gaussian and detects the peak and the edges of the Gaussian. Using the detected parameters across two samples, CGDWavelet classifies epigenetic changes. We applied CGDWavelet to the histone modification data from mouse embryonic stem cells (mESCs) and neural progenitor cells (mNPCs) and identified four groups of epigenetic changes. Associating each group with gene expression, we found that gene expression is affected by chromatin structure as well as the intensity of histone modification. We found that Smad1, Sox2 and Nanog but not Oct4 bind to the epigenetically variable regions for H3K4me3. Software is available at http://wonk.med.upenn.edu/CGDWavelet

Original languageEnglish (US)
Title of host publication2014 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2014
PublisherIEEE Computer Society
ISBN (Print)9781479945368
DOIs
StatePublished - Jan 1 2014
Externally publishedYes
Event2014 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2014 - Honolulu, HI, United States
Duration: May 21 2014May 24 2014

Publication series

Name2014 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2014

Other

Other2014 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2014
CountryUnited States
CityHonolulu, HI
Period5/21/145/24/14

Keywords

  • component
  • epigenome
  • histone modification
  • nucleosome
  • wavelet

ASJC Scopus subject areas

  • Artificial Intelligence
  • Health Informatics

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  • Cite this

    Nguyen, N., & Won, K. J. (2014). Gaussian derivative wavelets identify dynamic changes in histone modification. In 2014 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2014 [6845533] (2014 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2014). IEEE Computer Society. https://doi.org/10.1109/CIBCB.2014.6845533