HuMiTar: a sequence-based method for prediction of XXXX microRNA targets.

@entity1 @entity463 @entity1264 @entity11216
BACKGROUND: MicroRNAs (miRs) are small noncoding RNAs that bind to complementary/partially complementary sites in the 3' untranslated regions of target genes to regulate protein production of the target transcript and to induce mRNA degradation or mRNA cleavage. The ability to perform accurate, high-throughput identification of physiologically active @entity11216 targets would enable functional characterization of individual miRs. Current target prediction methods include traditional approaches that are based on specific base-pairing rules in the @entity11216 's seed region and implementation of cross-species conservation of the target site, and machine learning (ML) methods that explore patterns that contrast true and false @entity11216 -mRNA duplexes. However, in the case of the traditional methods research shows that some seed region matches that are conserved are false positives and that some of the experimentally validated target sites are not conserved. RESULTS: We present HuMiTar, a computational method for identifying common targets of miRs, which is based on a scoring function that considers base-pairing for both seed and non-seed positions for @entity1 @entity11216 -mRNA duplexes. Our design shows that certain non-seed @entity11216 nucleotides, such as 14, 18, 13, 11, and 17, are characterized by a strong bias towards formation of @entity1264 . We contrasted HuMiTar with several representative competing methods on two sets of @entity1 @entity11216 targets and a set of ten @entity463 oncogenes. Comparison with the two best performing traditional methods, PicTar and TargetScanS, and a representative ML method that considers the non-seed positions, NBmiRTar, shows that HuMiTar predictions include majority of the predictions of the other three methods. At the same time, the proposed method is also capable of finding more true positive targets as a trade-off for an increased number of predictions. Genome-wide predictions show that the proposed method is characterized by 1.99 signal-to-noise ratio and linear, with respect to the length of the mRNA sequence, computational complexity. The ROC analysis shows that HuMiTar obtains results comparable with PicTar, which are characterized by high true positive rates that are coupled with moderate values of false positive rates. CONCLUSION: The proposed HuMiTar method constitutes a step towards providing an efficient model for studying translational gene regulation by miRs.
