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

doi:http://dx.doi.org/10.1080/15476286.2021.1978215

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

Llicència

Citació

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

Resum

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.