Journal
GENOME BIOLOGY
Volume 20, Issue -, Pages -Publisher
BMC
DOI: 10.1186/s13059-019-1629-z
Keywords
MicroRNA; Target prediction; RNA-seq; CLIP-seq
Funding
- National Institutes of Health [R01GM089784, R01DE026471, R41HG008567]
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We perform a large-scale RNA sequencing study to experimentally identify genes that are downregulated by 25 miRNAs. This RNA-seq dataset is combined with public miRNA target binding data to systematically identify miRNA targeting features that are characteristic of both miRNA binding and target downregulation. By integrating these common features in a machine learning framework, we develop and validate an improved computational model for genome-wide miRNA target prediction. All prediction data can be accessed at miRDB (http://mirdb.org).
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