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Dissecting Nucleosome Free Regions by a Segmental Semi-Markov Model

Author

Listed:
  • Wei Sun
  • Wei Xie
  • Feng Xu
  • Michael Grunstein
  • Ker-Chau Li

Abstract

Background: Nucleosome free regions (NFRs) play important roles in diverse biological processes including gene regulation. A genome-wide quantitative portrait of each individual NFR, with their starting and ending positions, lengths, and degrees of nucleosome depletion is critical for revealing the heterogeneity of gene regulation and chromatin organization. By averaging nucleosome occupancy levels, previous studies have identified the presence of NFRs in the promoter regions across many genes. However, evaluation of the quantitative characteristics of individual NFRs requires an NFR calling method. Methodology: In this study, we propose a statistical method to identify the patterns of NFRs from a genome-wide measurement of nucleosome occupancy. This method is based on an appropriately designed segmental semi-Markov model, which can capture each NFR pattern and output its quantitative characterizations. Our results show that the majority of the NFRs are located in intergenic regions or promoters with a length of about 400–600bp and varying degrees of nucleosome depletion. Our quantitative NFR mapping allows for an investigation of the relative impacts of transcription machinery and DNA sequence in evicting histones from NFRs. We show that while both factors have significant overall effects, their specific contributions vary across different subtypes of NFRs. Conclusion: The emphasis of our approach on the variation rather than the consensus of nucleosome free regions sets the tone for enabling the exploration of many subtler dynamic aspects of chromatin biology.

Suggested Citation

  • Wei Sun & Wei Xie & Feng Xu & Michael Grunstein & Ker-Chau Li, 2009. "Dissecting Nucleosome Free Regions by a Segmental Semi-Markov Model," PLOS ONE, Public Library of Science, vol. 4(3), pages 1-10, March.
  • Handle: RePEc:plo:pone00:0004721
    DOI: 10.1371/journal.pone.0004721
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    Cited by:

    1. Alexander W. Blocker & Edoardo M. Airoldi, 2016. "Template-Based Models for Genome-Wide Analysis of Next-Generation Sequencing Data at Base-Pair Resolution," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(515), pages 967-987, July.

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