Computational methods for RNA modification detection from nanopore direct RNA sequencing data. (15th October 2021)

• Record Type: Journal Article
• Authors: Furlan, Mattia; Delgado-Tejedor, Anna; Mulroney, Logan; Pelizzola, Mattia; Novoa, Eva Maria; Leonardi, Tommaso
• Is Part Of: RNA biology. Volume 18(2021)Supplement 1
• Page Start: 31
• Page End: 40
• Publication Date: 2021-10-15
• Subjects: RNA modifications -- direct rna sequencing -- nanopore -- software -- epitranscriptome; RNA -- Periodicals; Molecular biology -- Periodicals; Molecular biology; RNA; Periodicals; 572.8805
• DOI: 10.1080/15476286.2021.1978215
• Languages: English
• ISSNs: 1547-6286

Abstract

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.