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Computational prediction of the localization of microRNAs within their pre-miRNA
MicroRNAs (miRNAs) are short RNA species derived from hairpin-forming miRNA precursors (pre-miRNA) and acting as key posttranscriptional regulators. Most computational tools labeled as miRNA predictors are in fact pre-miRNA predictors and provide no information about the putative miRNA location with...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3753617/ https://www.ncbi.nlm.nih.gov/pubmed/23748953 http://dx.doi.org/10.1093/nar/gkt466 |
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author | Leclercq, Mickael Diallo, Abdoulaye Banire Blanchette, Mathieu |
author_facet | Leclercq, Mickael Diallo, Abdoulaye Banire Blanchette, Mathieu |
author_sort | Leclercq, Mickael |
collection | PubMed |
description | MicroRNAs (miRNAs) are short RNA species derived from hairpin-forming miRNA precursors (pre-miRNA) and acting as key posttranscriptional regulators. Most computational tools labeled as miRNA predictors are in fact pre-miRNA predictors and provide no information about the putative miRNA location within the pre-miRNA. Sequence and structural features that determine the location of the miRNA, and the extent to which these properties vary from species to species, are poorly understood. We have developed miRdup, a computational predictor for the identification of the most likely miRNA location within a given pre-miRNA or the validation of a candidate miRNA. MiRdup is based on a random forest classifier trained with experimentally validated miRNAs from miRbase, with features that characterize the miRNA–miRNA* duplex. Because we observed that miRNAs have sequence and structural properties that differ between species, mostly in terms of duplex stability, we trained various clade-specific miRdup models and obtained increased accuracy. MiRdup self-trains on the most recent version of miRbase and is easy to use. Combined with existing pre-miRNA predictors, it will be valuable for both de novo mapping of miRNAs and filtering of large sets of candidate miRNAs obtained from transcriptome sequencing projects. MiRdup is open source under the GPLv3 and available at http://www.cs.mcgill.ca/∼blanchem/mirdup/. |
format | Online Article Text |
id | pubmed-3753617 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-37536172013-08-27 Computational prediction of the localization of microRNAs within their pre-miRNA Leclercq, Mickael Diallo, Abdoulaye Banire Blanchette, Mathieu Nucleic Acids Res Computational Biology MicroRNAs (miRNAs) are short RNA species derived from hairpin-forming miRNA precursors (pre-miRNA) and acting as key posttranscriptional regulators. Most computational tools labeled as miRNA predictors are in fact pre-miRNA predictors and provide no information about the putative miRNA location within the pre-miRNA. Sequence and structural features that determine the location of the miRNA, and the extent to which these properties vary from species to species, are poorly understood. We have developed miRdup, a computational predictor for the identification of the most likely miRNA location within a given pre-miRNA or the validation of a candidate miRNA. MiRdup is based on a random forest classifier trained with experimentally validated miRNAs from miRbase, with features that characterize the miRNA–miRNA* duplex. Because we observed that miRNAs have sequence and structural properties that differ between species, mostly in terms of duplex stability, we trained various clade-specific miRdup models and obtained increased accuracy. MiRdup self-trains on the most recent version of miRbase and is easy to use. Combined with existing pre-miRNA predictors, it will be valuable for both de novo mapping of miRNAs and filtering of large sets of candidate miRNAs obtained from transcriptome sequencing projects. MiRdup is open source under the GPLv3 and available at http://www.cs.mcgill.ca/∼blanchem/mirdup/. Oxford University Press 2013-08 2013-06-08 /pmc/articles/PMC3753617/ /pubmed/23748953 http://dx.doi.org/10.1093/nar/gkt466 Text en © The Author(s) 2013. Published by Oxford University Press. http://creativecommons.org/licenses/by/3.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Computational Biology Leclercq, Mickael Diallo, Abdoulaye Banire Blanchette, Mathieu Computational prediction of the localization of microRNAs within their pre-miRNA |
title | Computational prediction of the localization of microRNAs within their pre-miRNA |
title_full | Computational prediction of the localization of microRNAs within their pre-miRNA |
title_fullStr | Computational prediction of the localization of microRNAs within their pre-miRNA |
title_full_unstemmed | Computational prediction of the localization of microRNAs within their pre-miRNA |
title_short | Computational prediction of the localization of microRNAs within their pre-miRNA |
title_sort | computational prediction of the localization of micrornas within their pre-mirna |
topic | Computational Biology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3753617/ https://www.ncbi.nlm.nih.gov/pubmed/23748953 http://dx.doi.org/10.1093/nar/gkt466 |
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