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Bioinformatics 2009 25(12):i348-i355; doi:10.1093/bioinformatics/btp216
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© 2009 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Modeling interactions between adjacent nucleosomes improves genome-wide predictions of nucleosome occupancy

Shai Lubliner 1 and Eran Segal 1,2,*

1Department of Computer Science and Applied Mathematics and 2Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel

*To whom correspondence should be addressed.


  Abstract

Motivation: Understanding the mechanisms that govern nucleosome positioning over genomes in vivo is essential for unraveling the role of chromatin organization in transcriptional regulation. Until now, models for predicting genome-wide nucleosome occupancy have assumed that the DNA associations of neighboring nucleosomes on the genome are independent. We present a new model that relaxes this independence assumption by modeling interactions between adjacent nucleosomes.

Results: We show that modeling interactions between adjacent nucleosomes improves genome-wide nucleosome occupancy predictions in an in vitro system that includes only nucleosomes and purified DNA, where the resulting model has a preference for short spacings (linkers) of less than 20 bp in length between neighboring nucleosomes. Since nucleosome occupancy in vitro depends only on properties intrinsic to nucleosomes, these results suggest that the interactions we find are intrinsic to nucleosomes and do not depend on other factors, such as transcription factors and chromatin remodelers. We also show that modeling these intrinsic interactions significantly improves genome-wide predictions of nucleosome occupancy in vivo.

Contact: eran.segal{at}weizmann.ac.il

Supplementary information: Supplementary data are available at Bioinformatics online.


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