title: A statistical framework for modeling gene expression using chromatin features and application to modENCODE datasets.
abstract: 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.
authors.affiliation: Department of Molecular Biophysics and Biochemistry, Yale University, 260 Whitney Avenue, New Haven, CT 06520, USA.
authors.forename: Chao
authors.initials: C
authors.lastname: Cheng
authors.forename: Koon-Kiu
authors.initials: KK
authors.lastname: Yan
authors.forename: Kevin Y
authors.initials: KY
authors.lastname: Yip
authors.forename: Joel
authors.initials: J
authors.lastname: Rozowsky
authors.forename: Roger
authors.initials: R
authors.lastname: Alexander
authors.forename: Chong
authors.initials: C
authors.lastname: Shou
authors.forename: Mark
authors.initials: M
authors.lastname: Gerstein
mesh_terms: D000465:Algorithms; D000818:Animals; D017173:Caenorhabditis elegans / Q000235:genetics*; D002843:Chromatin / Q000235:genetics* / Q000378:metabolism; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D057225:Data Mining / Q000379:methods*; D015870:Gene Expression; D020869:Gene Expression Profiling; D005786:Gene Expression Regulation; D006657:Histones / Q000235:genetics* / Q000378:metabolism; D006801:Humans; D035683:MicroRNAs / Q000235:genetics* / Q000378:metabolism; D015233:Models, Statistical
chemical_list: D002843:Chromatin; D006657:Histones; D035683:MicroRNAs