isomiRs-Hidden Soldiers in the miRNA Regulatory Army, and How to Find Them?

Numerous studies on microRNAs (miRNA) in cancer and other diseases have been accompanied by diverse computational approaches and experimental methods to predict and validate miRNA biological and clinical significance as easily accessible disease biomarkers. In recent years, the application of the next-generation deep sequencing for the analysis and discovery of novel RNA biomarkers has clearly shown an expanding repertoire of diverse sequence variants of mature miRNAs, or isomiRs, resulting from alternative post-transcriptional processing events, and affected by (patho)physiological changes, population origin, individual's gender, and age. Here, we provide an in-depth overview of currently available bioinformatics approaches for the detection and visualization of both mature miRNA and cognate isomiR sequences. An attempt has been made to present in a systematic way the advantages and downsides of in silico approaches in terms of their sensitivity and accuracy performance, as well as used methods, workflows, and processing steps, and end output dataset overlapping issues. The focus is given to the challenges and pitfalls of isomiR expression analysis. Specifically, we address the availability of tools enabling research without extensive bioinformatics background to explore this fascinating corner of the small RNAome universe that may facilitate the discovery of new and more reliable disease biomarkers.

Automated summary

Research on microRNAs (miRNA) in cancer and other diseases has led to the development of diverse computational approaches and experimental methods for predicting and validating their biological and clinical significance as disease biomarkers. The use of next-generation deep sequencing has allowed for the discovery of new RNA biomarkers, including diverse sequence variants of mature miRNAs or isomiRs. However, challenges and pitfalls exist in the analysis of isomiR expression.