Discriminative prediction of A-To-I RNA editing events from DNA sequence
(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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- author
- 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
- organization
- publishing date
- 2016-10-01
- type
- Contribution to journal
- publication status
- published
- subject
- in
- PLoS ONE
- volume
- 11
- issue
- 10
- article number
- e0164962
- publisher
- Public Library of Science (PLoS)
- external identifiers
-
- scopus:84992346885
- pmid:27764195
- 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
- 2024-06-28 19:02:37
@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>}}, 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 (PLoS)}}, 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}}, doi = {{10.1371/journal.pone.0164962}}, volume = {{11}}, year = {{2016}}, }