RT Journal Article SR Electronic T1 Genome-Wide Prediction of cis-Regulatory Regions Using Supervised Deep Learning Methods JF bioRxiv FD Cold Spring Harbor Laboratory SP 041616 DO 10.1101/041616 A1 Yifeng Li A1 Wenqiang Shi A1 Wyeth W. Wasserman YR 2016 UL http://biorxiv.org/content/early/2016/02/28/041616.abstract AB Identifying active cis-regulatory regions in the human genome is critical for understanding gene regulation and assessing the impact of genetic variation on phenotype. Based on rich data resources such as the Encyclopedia of DNA Elements (ENCODE) and the Functional Annotation of the Mammalian Genome (FANTOM) projects, we introduce DECRES, the first supervised deep learning approach for the identification of enhancer and promoter regions in the human genome. Due to their ability to discover patterns in large and complex data, the introduction of deep learning methods enables a significant advance in our knowledge of the genomic locations of cis-regulatory regions. Using models for well-characterized cell lines, we identify key experimental features that contribute to the predictive performance. Applying DECRES, we delineate locations of 300,000 candidate enhancers genome wide (6.8% of the genome, of which 40,000 are supported by bidirectional transcription data) and 26,000 candidate promoters (0.6% of the genome).