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Sequence-based prediction of single nucleosome positioning and genome-wide nucleosome occupancy.

ABSTRACT: Nucleosome positioning dictates eukaryotic DNA compaction and access. To predict nucleosome positions in a statistical mechanics model, we exploited the knowledge that nucleosomes favor DNA sequences with specific periodically occurring dinucleotides. Our model is the first to capture both dyad position within a few base pairs, and free binding energy within 2 k(B)T, for all the known nucleosome positioning sequences. By applying Percus's equation to the derived energy landscape, we isolate sequence effects on genome-wide nucleosome occupancy from other factors that may influence nucleosome positioning. For both in vitro and in vivo systems, three parameters suffice to predict nucleosome occupancy with correlation coefficients of respectively 0.74 and 0.66. As predicted, we find the largest deviations in vivo around transcription start sites. This relatively simple algorithm can be used to guide future studies on the influence of DNA sequence on chromatin organization.

SUBMITTER: van der Heijden T

PROVIDER: S-EPMC3458375 | biostudies-literature | 2012 Sep

REPOSITORIES: biostudies-literature

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Sequence-based prediction of single nucleosome positioning and genome-wide nucleosome occupancy.

van der Heijden Thijn T van Vugt Joke J F A JJ Logie Colin C van Noort John J

Proceedings of the National Academy of Sciences of the United States of America 20120820 38

Nucleosome positioning dictates eukaryotic DNA compaction and access. To predict nucleosome positions in a statistical mechanics model, we exploited the knowledge that nucleosomes favor DNA sequences with specific periodically occurring dinucleotides. Our model is the first to capture both dyad position within a few base pairs, and free binding energy within 2 k(B)T, for all the known nucleosome positioning sequences. By applying Percus's equation to the derived energy landscape, we isolate sequ ...[more]

PMID: 22908247

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Project description:BACKGROUND:Nucleosome distribution along chromatin dictates genomic DNA accessibility and thus profoundly influences gene expression. However, the underlying mechanism of nucleosome formation remains elusive. Here, taking a structural perspective, we systematically explored nucleosome formation potential of genomic sequences and the effect on chromatin organization and gene expression in S. cerevisiae. RESULTS:We analyzed twelve structural features related to flexibility, curvature and energy of DNA sequences. The results showed that some structural features such as DNA denaturation, DNA-bending stiffness, Stacking energy, Z-DNA, Propeller twist and free energy, were highly correlated with in vitro and in vivo nucleosome occupancy. Specifically, they can be classified into two classes, one positively and the other negatively correlated with nucleosome occupancy. These two kinds of structural features facilitated nucleosome binding in centromere regions and repressed nucleosome formation in the promoter regions of protein-coding genes to mediate transcriptional regulation. Based on these analyses, we integrated all twelve structural features in a model to predict more accurately nucleosome occupancy in vivo than the existing methods that mainly depend on sequence compositional features. Furthermore, we developed a novel approach, named DLaNe, that located nucleosomes by detecting peaks of structural profiles, and built a meta predictor to integrate information from different structural features. As a comparison, we also constructed a hidden Markov model (HMM) to locate nucleosomes based on the profiles of these structural features. The result showed that the meta DLaNe and HMM-based method performed better than the existing methods, demonstrating the power of these structural features in predicting nucleosome positions. CONCLUSIONS:Our analysis revealed that DNA structures significantly contribute to nucleosome organization and influence chromatin structure and gene expression regulation. The results indicated that our proposed methods are effective in predicting nucleosome occupancy and positions and that these structural features are highly predictive of nucleosome organization.The implementation of our DLaNe method based on structural features is available online.

Dataset Information

Sequence-based prediction of single nucleosome positioning and genome-wide nucleosome occupancy.

Publications

Sequence-based prediction of single nucleosome positioning and genome-wide nucleosome occupancy.

Similar Datasets

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