Computational Detection of MicroRNA Targets

• Autor(a) principal: Nachtigall, Pedro Gabriel
• Data de Publicação: 2022
• Outros Autores: Bovolenta, Luiz Augusto [UNESP]
• Tipo de documento: Capítulo de livro
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1007/978-1-0716-1170-8_10; http://hdl.handle.net/11449/229443
• topic: Bioinformatics; Computational biology; miRNA recognition element; Noncoding RNA; Target prediction tools

MicroRNAs (miRNAs) are small noncoding RNAs that are recognized as posttranscriptional regulators of gene expression. These molecules have been shown to play important roles in several cellular processes. MiRNAs act on their target by guiding the RISC complex and binding to the mRNA molecule. Thus, it is recognized that the function of a miRNA is determined by the function of its target (s). By using high-throughput methodologies, novel miRNAs are being identified, but their functions remain uncharted. Target validation is crucial to properly understand the specific role of a miRNA in a cellular pathway. However, molecular techniques for experimental validation of miRNA–target interaction are expensive, time-consuming, laborious, and can be not accurate in inferring true interactions. Thus, accurate miRNA target predictions are helpful to understand the functions of miRNAs. There are several algorithms proposed for target prediction and databases containing miRNA-target information. However, these available computational tools for prediction still generate a large number of false positives and fail to detect a considerable number of true targets, which indicates the necessity of highly confident approaches to identify bona fide miRNA–target interactions. This chapter focuses on tools and strategies used for miRNA target prediction, by providing practical insights and outlooks.