Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Furlan, Mattia, Delgado-Tejedor, Anna, Mulroney, Logan, Pelizzola, Mattia, Novoa, Eva Maria, Leonardi, Tommaso
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Taylor & Francis 2021
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
_version_ 1784616058654556160
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
work_keys_str_mv AT furlanmattia computationalmethodsforrnamodificationdetectionfromnanoporedirectrnasequencingdata
AT delgadotejedoranna computationalmethodsforrnamodificationdetectionfromnanoporedirectrnasequencingdata
AT mulroneylogan computationalmethodsforrnamodificationdetectionfromnanoporedirectrnasequencingdata
AT pelizzolamattia computationalmethodsforrnamodificationdetectionfromnanoporedirectrnasequencingdata
AT novoaevamaria computationalmethodsforrnamodificationdetectionfromnanoporedirectrnasequencingdata
AT leonarditommaso computationalmethodsforrnamodificationdetectionfromnanoporedirectrnasequencingdata