isomiRs–Hidden Soldiers in the miRNA Regulatory Army, and How to Find Them?
1
Faculty of Biology, University of Plovdiv, Tzar Assen 24, 4000 Plovdiv, Bulgaria
2
Department of Neurosurgery, Cancer Center Amsterdam, Amsterdam University Medical Centers, VU University Medical Center, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands
*
Author to whom correspondence should be addressed.
Biomolecules 2021, 11(1), 41; https://doi.org/10.3390/biom11010041
Received: 27 November 2020
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Revised: 22 December 2020
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Accepted: 26 December 2020
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Published: 30 December 2020
(This article belongs to the Special Issue Bioinformatics Resource and Protocols for Small RNA Research)
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.
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Keywords:
isomiRs; microRNAs; NGS; tools; data analysis
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MDPI and ACS Style
Glogovitis, I.; Yahubyan, G.; Würdinger, T.; Koppers-Lalic, D.; Baev, V. isomiRs–Hidden Soldiers in the miRNA Regulatory Army, and How to Find Them? Biomolecules 2021, 11, 41. https://doi.org/10.3390/biom11010041
AMA Style
Glogovitis I, Yahubyan G, Würdinger T, Koppers-Lalic D, Baev V. isomiRs–Hidden Soldiers in the miRNA Regulatory Army, and How to Find Them? Biomolecules. 2021; 11(1):41. https://doi.org/10.3390/biom11010041
Chicago/Turabian StyleGlogovitis, Ilias, Galina Yahubyan, Thomas Würdinger, Danijela Koppers-Lalic, and Vesselin Baev. 2021. "isomiRs–Hidden Soldiers in the miRNA Regulatory Army, and How to Find Them?" Biomolecules 11, no. 1: 41. https://doi.org/10.3390/biom11010041
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