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Genomic Sequence Is Highly Predictive of Local Nucleosome Depletion

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  • Guo-Cheng Yuan
  • Jun S Liu

Abstract

The regulation of DNA accessibility through nucleosome positioning is important for transcription control. Computational models have been developed to predict genome-wide nucleosome positions from DNA sequences, but these models consider only nucleosome sequences, which may have limited their power. We developed a statistical multi-resolution approach to identify a sequence signature, called the N-score, that distinguishes nucleosome binding DNA from non-nucleosome DNA. This new approach has significantly improved the prediction accuracy. The sequence information is highly predictive for local nucleosome enrichment or depletion, whereas predictions of the exact positions are only modestly more accurate than a null model, suggesting the importance of other regulatory factors in fine-tuning the nucleosome positions. The N-score in promoter regions is negatively correlated with gene expression levels. Regulatory elements are enriched in low N-score regions. While our model is derived from yeast data, the N-score pattern computed from this model agrees well with recent high-resolution protein-binding data in human.Author Summary: A eukaryotic genome is packaged into chromatin. The chromatin not only makes it possible to fit the relatively long genome into a tiny nucleus, but also plays an important regulatory role. The nucleosome is the fundamental repeating unit of chromatin. High-resolution tiling array experiments have shown that many nucleosomes are well-positioned in vivo, consistent with an important regulatory role. However, the mechanisms that determine nucleosome positioning are still poorly understood. We have developed a novel computational method for predicting nucleosome positions using only the genomic sequence information. The method detects periodic sequence signatures that discriminate nucleosome sequences from linker sequences. We show that this approach has significantly improved predictive power compared to previous studies. Interestingly, the most predictable regions tend to be located where stringent regulations are needed, i.e., the neighborhood of a transcription start site. This model predicts that nucleosome occupancy is not strongly controlled by short DNA sequence motifs but rather progressively controlled by regular organization of short elements into periodic patterns. We also provide evidence that sequence specificity for nucleosome binding is conserved from yeast to human.

Suggested Citation

  • Guo-Cheng Yuan & Jun S Liu, 2008. "Genomic Sequence Is Highly Predictive of Local Nucleosome Depletion," PLOS Computational Biology, Public Library of Science, vol. 4(1), pages 1-11, January.
  • Handle: RePEc:plo:pcbi00:0040013
    DOI: 10.1371/journal.pcbi.0040013
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    References listed on IDEAS

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    1. Istvan Albert & Travis N. Mavrich & Lynn P. Tomsho & Ji Qi & Sara J. Zanton & Stephan C. Schuster & B. Franklin Pugh, 2007. "Translational and rotational settings of H2A.Z nucleosomes across the Saccharomyces cerevisiae genome," Nature, Nature, vol. 446(7135), pages 572-576, March.
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    4. Eran Segal & Yvonne Fondufe-Mittendorf & Lingyi Chen & AnnChristine Thåström & Yair Field & Irene K. Moore & Ji-Ping Z. Wang & Jonathan Widom, 2006. "A genomic code for nucleosome positioning," Nature, Nature, vol. 442(7104), pages 772-778, August.
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    1. Wei Chen & Hao Lin & Peng-Mian Feng & Chen Ding & Yong-Chun Zuo & Kuo-Chen Chou, 2012. "iNuc-PhysChem: A Sequence-Based Predictor for Identifying Nucleosomes via Physicochemical Properties," PLOS ONE, Public Library of Science, vol. 7(10), pages 1-9, October.
    2. Iksoo Huh & Isabel Mendizabal & Taesung Park & Soojin V Yi, 2018. "Functional conservation of sequence determinants at rapidly evolving regulatory regions across mammals," PLOS Computational Biology, Public Library of Science, vol. 14(10), pages 1-21, October.
    3. Wolfram Möbius & Ulrich Gerland, 2010. "Quantitative Test of the Barrier Nucleosome Model for Statistical Positioning of Nucleosomes Up- and Downstream of Transcription Start Sites," PLOS Computational Biology, Public Library of Science, vol. 6(8), pages 1-11, August.
    4. 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.
    5. Moser Carlee & Gupta Mayetri, 2012. "A Generalized Hidden Markov Model for Determining Sequence-based Predictors of Nucleosome Positioning," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(2), pages 1-23, January.

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