title: A statistical framework to predict functional non-coding regions in the human genome through integrated analysis of annotation data.
abstract: Identifying functional regions in the human genome is a major goal in human genetics. Great efforts have been made to functionally annotate the human genome either through computational predictions, such as genomic conservation, or high-throughput experiments, such as the ENCODE project. These efforts have resulted in a rich collection of functional annotation data of diverse types that need to be jointly analyzed for integrated interpretation and annotation. Here we present GenoCanyon, a whole-genome annotation method that performs unsupervised statistical learning using 22 computational and experimental annotations thereby inferring the functional potential of each position in the human genome. With GenoCanyon, we are able to predict many of the known functional regions. The ability of predicting functional regions as well as its generalizable statistical framework makes GenoCanyon a unique and powerful tool for whole-genome annotation. The GenoCanyon web server is available at http://genocanyon.med.yale.edu.
authors.affiliation: Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA.
authors.forename: Qiongshi
authors.initials: Q
authors.lastname: Lu
authors.affiliation: Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA.
authors.forename: Yiming
authors.initials: Y
authors.lastname: Hu
authors.affiliation: Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA.
authors.forename: Jiehuan
authors.initials: J
authors.lastname: Sun
authors.affiliation: Program of Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA.
authors.forename: Yuwei
authors.initials: Y
authors.lastname: Cheng
authors.affiliation: 1] Program of Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA [2] Yale Center for Medical Informatics, Yale School of Medicine, New Haven, CT, USA [3] Department of Emergency Medicine, Yale School of Medicine, New Haven, CT, USA [4] VA Connecticut Healthcare System, West Haven, CT, USA.
authors.forename: Kei-Hoi
authors.initials: KH
authors.lastname: Cheung
authors.affiliation: 1] Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA [2] Program of Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA [3] VA Connecticut Healthcare System, West Haven, CT, USA.
authors.forename: Hongyu
authors.initials: H
authors.lastname: Zhao
mesh_terms: D002880:Chromosomes, Human, Pair 11; D002897:Chromosomes, Human, Pair 7; D041321:Chromosomes, Human, X; D004742:Enhancer Elements, Genetic; D015894:Genome, Human; D053823:Hedgehog Proteins / Q000235:genetics; D006801:Humans; D008565:Membrane Proteins / Q000235:genetics; D008957:Models, Genetic; D058977:Molecular Sequence Annotation; D020641:Polymorphism, Single Nucleotide; D055544:beta-Globins / Q000235:genetics
chemical_list: D053823:Hedgehog Proteins; C485955:LMBR1 protein, human; D008565:Membrane Proteins; C509239:SHH protein, human; D055544:beta-Globins