Abstract
MicroRNAs (miRNAs) are a family of non-coding RNAs that play a central role in fine-tuning gene expression regulation. Over the past decade, identification and annotation of miRNAs have become a major focus in epigenomics research. However, detection and characterization of miRNA are challenging due to its small size (similar to 22 nucleotide-long) and susceptibility to degradation. The difficulties involved in experimental prediction and characterization of miRNA coding genes have led to the development of in silico-based approaches. Although several algorithms have been developed in recent years, a comprehensive assessment of the principles, methodological insights, and estimate of the strengths and weaknesses of computational methods are limited. The present review is dealt with the detailed methodological insights of different tools used for identifying miRNA coding genes falling under four computational approaches. The parameters considered in these tools along with their specificity are also delineated. Furthermore, the strengths and weaknesses of these four computational approaches, and the bioinformatics resources pertaining to target identification, expression analysis, regulatory network analysis, and SNP identification are stated in this review. The methodological details of miRNA prediction methods and bioinformatics resources related to miRNA research in one platform would facilitate the miRNA research community to develop efficient tools for uncovering novel miRNAs and understanding their role in regulatory networks.