PT  - JOURNAL ARTICLE
AU  - Qiongshi Lu
AU  - Yiming Hu
AU  - Jiehuan Sun
AU  - Yuwei Cheng
AU  - Kei-Hoi Cheung
AU  - Hongyu Zhao
TI  - A Statistical Framework to Predict Functional Non-Coding Regions in the Human Genome Through Integrated Analysis of Annotation Data
AID  - 10.1101/018093
DP  - 2015 Jan 01
TA  - bioRxiv
PG  - 018093
4099  - http://biorxiv.org/content/early/2015/04/15/018093.short
4100  - http://biorxiv.org/content/early/2015/04/15/018093.full
AB  - 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