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. Epub 2021 Sep 24.

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.

Keywords: RNA modifications; direct rna sequencing; epitranscriptome; nanopore; software.

Publication types

  • Research Support, Non-U.S. Gov't
  • Review

MeSH terms

  • Algorithms*
  • Animals
  • Computational Biology / methods*
  • Humans
  • Nanopore Sequencing / methods*
  • RNA / chemistry*
  • RNA / genetics*
  • RNA Processing, Post-Transcriptional*
  • Software
  • Transcriptome*

Substances

  • RNA

Grants and funding

This paper was based upon work from COST Action CA16120 EPITRAN, supported by COST (European Cooperation in Science and Technology). AD-T is supported by an FPI Severo-Ochoa fellowship by the Spanish Ministry of Economy, Industry and Competitiveness (MEIC). This work was partly supported by funds from the Spanish Ministry of Economy, Industry and Competitiveness (MEIC) (PGC2018-098152-A-100 to EMN), and by funds from the Italian Association for Cancer Research (AIRC, project IG 2020, ID. 24784 to MP). We acknowledge the support of the MEIC to the EMBL partnership, Centro de Excelencia Severo Ochoa and CERCA Programme/Generalitat de Catalunya.