A probabilistic framework to improve microrna target prediction by incorporating proteomics data.
Due to the difficulties in identifying microRNA (miRNA) targets experimentally in a high-throughput manner, several computational approaches have been proposed. To this date, most leading algorithms are based on sequence information alone. However, there has been limited overlap between these predictions, implying high false-positive rates, which underlines the limitation of sequence-based approaches. Considering the repressive nature of miRNAs at the mRNA translational level, here we describe a probabilistic model to make predictions by combining sequence complementarity, miRNA expression level, and protein abundance. Our underlying assumption is that, given sequence complementarity between a miRNA and its putative mRNA targets, the miRNA expression level should be high and the protein abundance of the mRNA should be low. Having identified a set of confident predictions, we then built a second probabilistic model to trace back to the mRNA expression of the confident targets to investigate the mechanisms of the miRNA-mediated post-transcriptional regulation. Our results suggest that translational repression (which has no effect on mRNA level), instead of mRNA degradation, is the dominant mechanism in miRNA regulation. This observation explained the previously observed discordant correlation between mRNA expression and protein abundance.
Li
Jingjing
J
Department of Molecular Genetics, Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Ontario, Canada. jj.li@utoronto.ca
Min
Renqiang
R
Bonner
Anthony
A
Zhang
Zhaolei
Z
D000465:Algorithms; D001483:Base Sequence; D003198:Computer Simulation; D018390:Gene Targeting / Q000379:methods*; D035683:MicroRNAs / Q000235:genetics*; D008957:Models, Genetic; D015233:Models, Statistical; D008969:Molecular Sequence Data; D020543:Proteome / Q000235:genetics*; D017423:Sequence Analysis, RNA / Q000379:methods*
D035683:MicroRNAs; D020543:Proteome