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

Mattia FurlanAnna Delgado-TejedorLogan MulroneyMattia PelizzolaEva Maria NovoaTommaso Leonardi
Published in: RNA biology (2021)
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.
Keyphrases
  • single molecule
  • single cell
  • rna seq
  • atomic force microscopy
  • machine learning
  • nucleic acid
  • living cells
  • electronic health record
  • gene expression
  • deep learning
  • loop mediated isothermal amplification
  • solid state