TY - JOUR T1 - A Statistical Framework to Predict Functional Non-Coding Regions in the Human Genome Through Integrated Analysis of Annotation Data JF - bioRxiv DO - 10.1101/018093 SP - 018093 AU - Qiongshi Lu AU - Yiming Hu AU - Jiehuan Sun AU - Yuwei Cheng AU - Kei-Hoi Cheung AU - Hongyu Zhao Y1 - 2015/01/01 UR - http://biorxiv.org/content/early/2015/04/15/018093.abstract N2 - 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 ER -