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

Sun, Jiangming LU ; De Marinis, Yang LU ; Osmark, Peter LU ; Singh, Pratibha LU ; Bagge, Annika LU ; Valtat, Berengere LU ; Vikman, Petter LU ; Spégel, Peter LU and Mulder, Hindrik LU (2016) In PLoS ONE 11(10).
Abstract

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... (More)

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.

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organization
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publication status
published
subject
in
PLoS ONE
volume
11
issue
10
publisher
Public Library of Science
external identifiers
  • scopus:84992346885
  • wos:000386204500085
ISSN
1932-6203
DOI
10.1371/journal.pone.0164962
language
English
LU publication?
yes
id
9ed78d74-f7d7-4746-b806-8b59150ecf8e
date added to LUP
2016-11-15 12:30:20
date last changed
2017-01-01 08:39:39
@article{9ed78d74-f7d7-4746-b806-8b59150ecf8e,
  abstract     = {<p>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.</p>},
  articleno    = {e0164962},
  author       = {Sun, Jiangming and De Marinis, Yang and Osmark, Peter and Singh, Pratibha and Bagge, Annika and Valtat, Berengere and Vikman, Petter and Spégel, Peter and Mulder, Hindrik},
  issn         = {1932-6203},
  language     = {eng},
  month        = {10},
  number       = {10},
  publisher    = {Public Library of Science},
  series       = {PLoS ONE},
  title        = {Discriminative prediction of A-To-I RNA editing events from DNA sequence},
  url          = {http://dx.doi.org/10.1371/journal.pone.0164962},
  volume       = {11},
  year         = {2016},
}