Small RNA Targets: Advances in Prediction Tools and High-Throughput Profiling

• Published in: Biology (Basel, Switzerland) Vol. 11; no. 12; p. 1798
• Main Authors: Grešová, Katarína, Alexiou, Panagiotis, Giassa, Ilektra-Chara
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 11-12-2022; MDPI
• Subjects: Bioinformatics; Biosynthesis; computational biology; Computer applications; Gene expression; high-throughput sequencing; Learning algorithms; Machine learning; MicroRNAs; miRNA; miRNA target prediction; Next-generation sequencing; Post-transcription; Predictions; Proteins; Review; Seeds; siRNA; small RNA target prediction; Transfer RNA; computational biology; machine learning; small RNA target prediction; miRNA target prediction; high-throughput sequencing
• ISSN: 2079-7737; 2079-7737
• DOI: 10.3390/biology11121798

MicroRNAs (miRNAs) are an abundant class of small non-coding RNAs that regulate gene expression at the post-transcriptional level. They are suggested to be involved in most biological processes of the cell primarily by targeting messenger RNAs (mRNAs) for cleavage or translational repression. Their binding to their target sites is mediated by the Argonaute (AGO) family of proteins. Thus, miRNA target prediction is pivotal for research and clinical applications. Moreover, transfer-RNA-derived fragments (tRFs) and other types of small RNAs have been found to be potent regulators of Ago-mediated gene expression. Their role in mRNA regulation is still to be fully elucidated, and advancements in the computational prediction of their targets are in their infancy. To shed light on these complex RNA-RNA interactions, the availability of good quality high-throughput data and reliable computational methods is of utmost importance. Even though the arsenal of computational approaches in the field has been enriched in the last decade, there is still a degree of discrepancy between the results they yield. This review offers an overview of the relevant advancements in the field of bioinformatics and machine learning and summarizes the key strategies utilized for small RNA target prediction. Furthermore, we report the recent development of high-throughput sequencing technologies, and explore the role of non-miRNA AGO driver sequences.