Motivation: Systematic identification of microRNA (miRNA) targets remains a challenge. The miRNA overexpression coupled with genome-wide expression profiling is a promising new approach and calls for a new method that integrates expression and sequence information.
Results: We developed a probabilistic scoring method called targetScore. TargetScore infers miRNA targets as the transformed fold-changes weighted by the Bayesian posteriors given observed target features. To this end, we compiled 84 datasets from Gene Expression Omnibus corresponding to 77 human tissue or cells and 113 distinct transfected miRNAs. Comparing with other methods, targetScore achieves significantly higher accuracy in identifying known targets in most tests. Moreover, the confidence targets from targetScore exhibit comparable protein downregulation and are more significantly enriched for Gene Ontology terms. Using targetScore, we explored oncomir-oncogenes network and predicted several potential cancer-related miRNA-messenger RNA interactions.
Availability and implementation: TargetScore is available at Bioconductor: http://www.bioconductor.org/packages/devel/bioc/html/TargetScore.html.