Furlan, MattiaDelgado-Tejedor, AnnaMulroney, LoganPelizzola, MattiaNovoa, Eva MariaLeonardi, Tommaso2022-01-312022-01-312021Furlan M, Delgado-Tejedor A, Mulroney L, Pelizzola M, Novoa EM, Leonardi T. Computational methods for RNA modification detection from nanopore direct RNA sequencing data. RNA Biol. 2021 Oct 15;18(sup1):31-40. DOI: 10.1080/15476286.2021.19782151547-6286http://hdl.handle.net/10230/52372The covalent modification of RNA molecules is a pervasive feature of all classes of RNAs and has fundamental roles in the regulation of several cellular processes. Mapping the location of RNA modifications transcriptome-wide is key to unveiling their role and dynamic behaviour, but technical limitations have often hampered these efforts. Nanopore direct RNA sequencing is a third-generation sequencing technology that allows the sequencing of native RNA molecules, thus providing a direct way to detect modifications at single-molecule resolution. Despite recent advances, the analysis of nanopore sequencing data for RNA modification detection is still a complex task that presents many challenges. Many works have addressed this task using different approaches, resulting in a large number of tools with different features and performances. Here we review the diverse approaches proposed so far and outline the principles underlying currently available algorithms.application/pdfeng© 2021 Mattia Furlan et al. Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any wayRNANanoporProgramariinfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1080/15476286.2021.1978215info:eu-repo/semantics/openAccess