Abstract
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).
Highlights
MicroRNAs are small noncoding RNAs that are extensively involved in many diverse biological processes, and dysregulation of miRNA expression may lead to a variety of diseases [1]
Many common features have been discovered for miRNA target regulation, such as perfect pairing of the miRNA 5′-end to the target site, as well as relatively low GC content of
All commonly used target prediction algorithms were trained with various high-throughput profiling data, including microarray profiling data [14, 15], or more recently with crosslinking and immunoprecipitation (CLIP) sequencing data [16,17,18]
Summary
MicroRNAs (miRNAs) are small noncoding RNAs that are extensively involved in many diverse biological processes, and dysregulation of miRNA expression may lead to a variety of diseases [1]. For functional miRNA analysis, one critical first step is to identify genes targeted by the miRNA. To this end, most studies rely on computational tools to initially identify promising target candidates, which are subject to experimental validation at a later stage. Given the critical role of target prediction in miRNA functional characterization, many computational tools have been developed in the past 10 years, with gradually improved performance on target identification. High-quality training data are essential to identify relevant target features and to properly weight and combine these features for building the final prediction models. Recent improvements in CLIP studies further allow unambiguous identification of paired miRNA-target transcripts that
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