Popular Computational Tools Used for miRNA Prediction and Their Future Development Prospects

MicroRNAs (miRNAs) are 19-24 nucleotide (nt)-long noncoding, single-stranded RNA molecules that play significant roles in regulating the gene expression, growth, and development of plants and animals. From the year that miRNAs were first discovered until the beginning of the twenty-first century, researchers used experimental methods such as cloning and sequencing to identify new miRNAs and their roles in the posttranscriptional regulation of protein synthesis. Later, in the early 2000s, informatics approaches to the discovery of new miRNAs began to be implemented. With increasing knowledge about miRNA, more efficient algorithms have been developed for computational miRNA prediction. The miRNA research community, hoping for greater coverage and faster results, has shifted from cumbersome and expensive traditional experimental approaches to computational approaches. These computational methods started with homology-based comparisons of known miRNAs with orthologs in the genomes of other species; this method could identify a known miRNA in new species. Second-generation sequencing and next-generation sequencing of mRNA at different developmental stages and in specific tissues, in combination with a better search and alignment algorithm, have accelerated the process of predicting novel miRNAs in a particular species. Using the accumulated annotated miRNA sequence information, researchers have been able to design ab initio algorithms for miRNA prediction independent of genome sequence knowledge. Here, the methods recently used for miRNA computational prediction are summarized and classified into the following four categories: homology-based, target-based, scoring-based, and machine-learning-based approaches. Finally, the future developmental directions of miRNA prediction methods are discussed.