Computational Biology in microRNA.

MicroRNA (miRNA) is a class of small endogenous noncoding RNA species, which regulate gene expression post-transcriptionally by forming imperfect base-pair at the 3' untranslated regions of the messenger RNAs. Since the 1993 discovery of the first miRNA let-7 in worms, a vast number of studies have been dedicated to functionally characterizing miRNAs with a special emphasis on their roles in cancer. A single miRNA can potentially target ∼ 400 distinct genes, and there are over a 1000 distinct endogenous miRNAs in the human genome. Thus, miRNAs are likely involved in virtually all biological processes and pathways including carcinogenesis. However, functionally characterizing miRNAs hinges on the accurate identification of their mRNA targets, which has been a challenging problem due to imperfect base-pairing and condition-specific miRNA regulatory dynamics. In this review, we will survey the current state-of-the-art computational methods to predict miRNA targets, which are divided into three main categories: (1) sequence-based methods that primarily utilizes the canonical seed-match model, evolutionary conservation, and binding energy; (2) expression-based target prediction methods using the increasingly available miRNA and mRNA expression data measured for the same sample; and (3) network-based method that aims identify miRNA regulatory modules, which reflect their synergism in conferring a global impact to the biological system of interest. We hope that the review will serve as a good reference to the new comers to the ever-growing miRNA research field as well as veterans, who would appreciate the detailed review on the technicalities, strength, and limitations of each representative computational method.