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Prediction of novel microRNA genes in cancer-associated genomic regions—a combined computational and experimental approach

The majority of existing computational tools rely on sequence homology and/or structural similarity to identify novel microRNA (miRNA) genes. Recently supervised algorithms are utilized to address this problem, taking into account sequence, structure and comparative genomics information. In most of...

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Autores principales: Oulas, Anastasis, Boutla, Alexandra, Gkirtzou, Katerina, Reczko, Martin, Kalantidis, Kriton, Poirazi, Panayiota
Formato: Texto
Lenguaje:English
Publicado: Oxford University Press 2009
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2691815/
https://www.ncbi.nlm.nih.gov/pubmed/19324892
http://dx.doi.org/10.1093/nar/gkp120
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author Oulas, Anastasis
Boutla, Alexandra
Gkirtzou, Katerina
Reczko, Martin
Kalantidis, Kriton
Poirazi, Panayiota
author_facet Oulas, Anastasis
Boutla, Alexandra
Gkirtzou, Katerina
Reczko, Martin
Kalantidis, Kriton
Poirazi, Panayiota
author_sort Oulas, Anastasis
collection PubMed
description The majority of existing computational tools rely on sequence homology and/or structural similarity to identify novel microRNA (miRNA) genes. Recently supervised algorithms are utilized to address this problem, taking into account sequence, structure and comparative genomics information. In most of these studies miRNA gene predictions are rarely supported by experimental evidence and prediction accuracy remains uncertain. In this work we present a new computational tool (SSCprofiler) utilizing a probabilistic method based on Profile Hidden Markov Models to predict novel miRNA precursors. Via the simultaneous integration of biological features such as sequence, structure and conservation, SSCprofiler achieves a performance accuracy of 88.95% sensitivity and 84.16% specificity on a large set of human miRNA genes. The trained classifier is used to identify novel miRNA gene candidates located within cancer-associated genomic regions and rank the resulting predictions using expression information from a full genome tiling array. Finally, four of the top scoring predictions are verified experimentally using northern blot analysis. Our work combines both analytical and experimental techniques to show that SSCprofiler is a highly accurate tool which can be used to identify novel miRNA gene candidates in the human genome. SSCprofiler is freely available as a web service at http://www.imbb.forth.gr/SSCprofiler.html.
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spelling pubmed-26918152009-07-17 Prediction of novel microRNA genes in cancer-associated genomic regions—a combined computational and experimental approach Oulas, Anastasis Boutla, Alexandra Gkirtzou, Katerina Reczko, Martin Kalantidis, Kriton Poirazi, Panayiota Nucleic Acids Res Computational Biology The majority of existing computational tools rely on sequence homology and/or structural similarity to identify novel microRNA (miRNA) genes. Recently supervised algorithms are utilized to address this problem, taking into account sequence, structure and comparative genomics information. In most of these studies miRNA gene predictions are rarely supported by experimental evidence and prediction accuracy remains uncertain. In this work we present a new computational tool (SSCprofiler) utilizing a probabilistic method based on Profile Hidden Markov Models to predict novel miRNA precursors. Via the simultaneous integration of biological features such as sequence, structure and conservation, SSCprofiler achieves a performance accuracy of 88.95% sensitivity and 84.16% specificity on a large set of human miRNA genes. The trained classifier is used to identify novel miRNA gene candidates located within cancer-associated genomic regions and rank the resulting predictions using expression information from a full genome tiling array. Finally, four of the top scoring predictions are verified experimentally using northern blot analysis. Our work combines both analytical and experimental techniques to show that SSCprofiler is a highly accurate tool which can be used to identify novel miRNA gene candidates in the human genome. SSCprofiler is freely available as a web service at http://www.imbb.forth.gr/SSCprofiler.html. Oxford University Press 2009-06 2009-03-25 /pmc/articles/PMC2691815/ /pubmed/19324892 http://dx.doi.org/10.1093/nar/gkp120 Text en © 2009 The Author(s) http://creativecommons.org/licenses/by-nc/2.0/uk/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Computational Biology
Oulas, Anastasis
Boutla, Alexandra
Gkirtzou, Katerina
Reczko, Martin
Kalantidis, Kriton
Poirazi, Panayiota
Prediction of novel microRNA genes in cancer-associated genomic regions—a combined computational and experimental approach
title Prediction of novel microRNA genes in cancer-associated genomic regions—a combined computational and experimental approach
title_full Prediction of novel microRNA genes in cancer-associated genomic regions—a combined computational and experimental approach
title_fullStr Prediction of novel microRNA genes in cancer-associated genomic regions—a combined computational and experimental approach
title_full_unstemmed Prediction of novel microRNA genes in cancer-associated genomic regions—a combined computational and experimental approach
title_short Prediction of novel microRNA genes in cancer-associated genomic regions—a combined computational and experimental approach
title_sort prediction of novel microrna genes in cancer-associated genomic regions—a combined computational and experimental approach
topic Computational Biology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2691815/
https://www.ncbi.nlm.nih.gov/pubmed/19324892
http://dx.doi.org/10.1093/nar/gkp120
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