Discriminative Prediction of A-To-I RNA Editing Events from DNA Sequence
| Autor: | Bérengère Valtat, Hindrik Mulder, Jiangming Sun, Pratibha Singh, Yang De Marinis, Peter Osmark, Petter Vikman, Peter Spégel, Annika Bagge |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
0301 basic medicine
RNA editing Adenosine Molecular biology lcsh:Medicine Biochemistry Machine Learning chemistry.chemical_compound Database and Informatics Methods Mice Protein structure Sequencing techniques Invertebrate Genomics lcsh:Science Genetics Multidisciplinary Mammalian Genomics RNA sequencing Genomics Animal Models Genomic Databases Nucleic acids Sequence Analysis Research Article In silico Sequence Databases Mouse Models Biology Research and Analysis Methods DNA sequencing Deep sequencing Human Genomics 03 medical and health sciences Model Organisms Animals Humans Computer Simulation Biology and life sciences Base Sequence Genome Human lcsh:R RNA Computational Biology DNA Genome Analysis Inosine 030104 developmental biology Biological Databases Molecular biology techniques chemistry Animal Genomics lcsh:Q Human genome |
| Zdroj: | PLoS ONE PLoS ONE, Vol 11, Iss 10, p e0164962 (2016) |
| ISSN: | 1932-6203 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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