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Computational methods for RNA modification detection from nanopore direct RNA sequencing data

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dc.contributor.author Furlan, Mattia
dc.contributor.author Delgado-Tejedor, Anna
dc.contributor.author Mulroney, Logan
dc.contributor.author Pelizzola, Mattia
dc.contributor.author Novoa, Eva Maria
dc.contributor.author Leonardi, Tommaso
dc.date.accessioned 2022-01-31T12:23:29Z
dc.date.available 2022-01-31T12:23:29Z
dc.date.issued 2021
dc.identifier.citation Furlan 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: 10.1080/15476286.2021.1978215
dc.identifier.issn 1547-6286
dc.identifier.uri http://hdl.handle.net/10230/52372
dc.description.abstract The 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.
dc.description.sponsorship This paper was based upon work from COST Action CA16120 EPITRAN, supported by COST (European Cooperation in Science and Technology). AD-T is supported by an FPI Severo-Ochoa fellowship by the Spanish Ministry of Economy, Industry and Competitiveness (MEIC). This work was partly supported by funds from the Spanish Ministry of Economy, Industry and Competitiveness (MEIC) (PGC2018-098152-A-100 to EMN), and by funds from the Italian Association for Cancer Research (AIRC, project IG 2020, ID. 24784 to MP). We acknowledge the support of the MEIC to the EMBL partnership, Centro de Excelencia Severo Ochoa and CERCA Programme/Generalitat de Catalunya
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Taylor and Francis
dc.rights © 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 way
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject.other RNA
dc.subject.other Nanopor
dc.subject.other Programari
dc.title Computational methods for RNA modification detection from nanopore direct RNA sequencing data
dc.type info:eu-repo/semantics/article
dc.identifier.doi http://dx.doi.org/10.1080/15476286.2021.1978215
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion


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