A review of computational tools in microRNA discovery

• Main Authors: Clarissa Pedrosa Da Costa Gomes, Ji-Hoon eCho, Leroy E Hood, Octavio Luiz Franco, Rinaldo Wellerson Pereira, Kai eWang
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2013-05-01
• Series: Frontiers in Genetics
• Subjects: machine learning; RNA secondary structure; isomiRs; Sequence Homology; miRNA conservation
• Online Access: http://journal.frontiersin.org/Journal/10.3389/fgene.2013.00081/full

Since microRNAs (miRNAs) were discovered, their impact on regulating various biological activities has been a surprising and exciting field. Knowing the entire repertoire of these small molecules is the first step to gain a better understanding of their function. High throughput discovery tools such as next-generation sequencing significantly increased the number of miRNAs in different organisms in recent years. However, the process of being able to accurately identify miRNA is still a complex and difficult task, requiring the integration of experimental approaches with computational methods. A number of prediction algorisms based on characteristics of miRNA molecules have been developed to identify new miRNA species. Different approaches have certain strengths and weaknesses and, in this review, we aim to summarize several commonly used tools in miRNA discovery and provide some prospects on future developments.