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A statistical framework for modeling gene expression using chromatin features and application to modENCODE datasets

Chao Cheng1, Koon-Kiu Yan1, Kevin Y Yip12, Joel Rozowsky1, Roger Alexander1, Chong Shou1 and Mark Gerstein134*

Author Affiliations

1 Department of Molecular Biophysics and Biochemistry, Yale University, 260 Whitney Avenue, New Haven, CT 06520, USA

2 Department of Computer Science and Engineering, The Chinese University of Hong Kong, Rm 1006, Ho Sin-Hang Engineering Bldg, Shatin, New Territories, Hong Kong

3 Program in Computational Biology and Bioinformatics, Yale University, 260 Whitney Avenue, New Haven, CT 06520, USA

4 Department of Computer Science, Yale University, PO Box 208285, New Haven, CT 06520, USA

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Genome Biology 2011, 12:R15  doi:10.1186/gb-2011-12-2-r15

Published: 16 February 2011


We develop a statistical framework to study the relationship between chromatin features and gene expression. This can be used to predict gene expression of protein coding genes, as well as microRNAs. We demonstrate the prediction in a variety of contexts, focusing particularly on the modENCODE worm datasets. Moreover, our framework reveals the positional contribution around genes (upstream or downstream) of distinct chromatin features to the overall prediction of expression levels.