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ludc webb

Jiangming Sun

Bioinformatician

ludc webb

Discriminative prediction of A-To-I RNA editing events from DNA sequence

Author

  • Jiangming Sun
  • Yang De Marinis
  • Peter Osmark
  • Pratibha Singh
  • Annika Bagge
  • Berengere Valtat
  • Petter Vikman
  • Peter Spégel
  • Hindrik Mulder

Summary, in English

RNA editing is a post-transcriptional alteration of RNA sequences that, via insertions, deletions or base substitutions, can affect protein structure as well as RNA and protein expression. Recently, it has been suggested that RNA editing may be more frequent than previously thought. A great impediment, however, to a deeper understanding of this process is the paramount sequencing effort that needs to be undertaken to identify RNA editing events. Here, we describe an in silico approach, based on machine learning, that ameliorates this problem. Using 41 nucleotide long DNA sequences, we show that novel A-to-I RNA editing events can be predicted from known A-to-I RNA editing events intra- and interspecies. The validity of the proposed method was verified in an independent experimental dataset. Using our approach, 203 202 putative A-to-I RNA editing events were predicted in the whole human genome. Out of these, 9% were previously reported. The remaining sites require further validation, e.g., by targeted deep sequencing. In conclusion, the approach described here is a useful tool to identify potential A-to-I RNA editing events without the requirement of extensive RNA sequencing.

Department/s

  • Diabetes - Molecular Metabolism
  • Genomics, Diabetes and Endocrinology
  • Breastcancer-genetics
  • Centre for Analysis and Synthesis
  • EXODIAB: Excellence in Diabetes Research in Sweden

Publishing year

2016-10-01

Language

English

Publication/Series

PLoS ONE

Volume

11

Issue

10

Document type

Journal article

Publisher

Public Library of Science

Topic

  • Cell and Molecular Biology

Status

Published

Research group

  • Diabetes - Molecular Metabolism
  • Genomics, Diabetes and Endocrinology

ISBN/ISSN/Other

  • ISSN: 1932-6203