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.

Furlan, M., Delgado-Tejedor, A., Mulroney, L., Pelizzola, M., Novoa, E., Leonardi, T. (2021). Computational methods for RNA modification detection from nanopore direct RNA sequencing data. RNA BIOLOGY, 18(S1), 31-40 [10.1080/15476286.2021.1978215].

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

Pelizzola M
Co-ultimo
;
2021

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.
Articolo in rivista - Articolo scientifico
direct rna sequencing; epitranscriptome; nanopore; RNA modifications; software;
English
2021
18
S1
31
40
open
Furlan, M., Delgado-Tejedor, A., Mulroney, L., Pelizzola, M., Novoa, E., Leonardi, T. (2021). Computational methods for RNA modification detection from nanopore direct RNA sequencing data. RNA BIOLOGY, 18(S1), 31-40 [10.1080/15476286.2021.1978215].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/446678
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