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Computational methods for RNA modification detection from nanopore direct RNA sequencing data
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 limitat...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Taylor & Francis
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8677041/ https://www.ncbi.nlm.nih.gov/pubmed/34559589 http://dx.doi.org/10.1080/15476286.2021.1978215 |
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author | Furlan, Mattia Delgado-Tejedor, Anna Mulroney, Logan Pelizzola, Mattia Novoa, Eva Maria Leonardi, Tommaso |
author_facet | Furlan, Mattia Delgado-Tejedor, Anna Mulroney, Logan Pelizzola, Mattia Novoa, Eva Maria Leonardi, Tommaso |
author_sort | Furlan, Mattia |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8677041 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Taylor & Francis |
record_format | MEDLINE/PubMed |
spelling | pubmed-86770412022-02-07 Computational methods for RNA modification detection from nanopore direct RNA sequencing data Furlan, Mattia Delgado-Tejedor, Anna Mulroney, Logan Pelizzola, Mattia Novoa, Eva Maria Leonardi, Tommaso RNA Biol Review 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. Taylor & Francis 2021-09-24 /pmc/articles/PMC8677041/ /pubmed/34559589 http://dx.doi.org/10.1080/15476286.2021.1978215 Text en © 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. https://creativecommons.org/licenses/by-nc-nd/4.0/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/ (https://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. |
spellingShingle | Review Furlan, Mattia Delgado-Tejedor, Anna Mulroney, Logan Pelizzola, Mattia Novoa, Eva Maria Leonardi, Tommaso Computational methods for RNA modification detection from nanopore direct RNA sequencing data |
title | Computational methods for RNA modification detection from nanopore direct RNA sequencing data |
title_full | Computational methods for RNA modification detection from nanopore direct RNA sequencing data |
title_fullStr | Computational methods for RNA modification detection from nanopore direct RNA sequencing data |
title_full_unstemmed | Computational methods for RNA modification detection from nanopore direct RNA sequencing data |
title_short | Computational methods for RNA modification detection from nanopore direct RNA sequencing data |
title_sort | computational methods for rna modification detection from nanopore direct rna sequencing data |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8677041/ https://www.ncbi.nlm.nih.gov/pubmed/34559589 http://dx.doi.org/10.1080/15476286.2021.1978215 |
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