Cargando…

miREE: miRNA recognition elements ensemble

BACKGROUND: Computational methods for microRNA target prediction are a fundamental step to understand the miRNA role in gene regulation, a key process in molecular biology. In this paper we present miREE, a novel microRNA target prediction tool. miREE is an ensemble of two parts entailing complement...

Descripción completa

Detalles Bibliográficos
Autores principales: Reyes-Herrera, Paula H, Ficarra, Elisa, Acquaviva, Andrea, Macii, Enrico
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3265527/
https://www.ncbi.nlm.nih.gov/pubmed/22115078
http://dx.doi.org/10.1186/1471-2105-12-454
_version_ 1782222109568663552
author Reyes-Herrera, Paula H
Ficarra, Elisa
Acquaviva, Andrea
Macii, Enrico
author_facet Reyes-Herrera, Paula H
Ficarra, Elisa
Acquaviva, Andrea
Macii, Enrico
author_sort Reyes-Herrera, Paula H
collection PubMed
description BACKGROUND: Computational methods for microRNA target prediction are a fundamental step to understand the miRNA role in gene regulation, a key process in molecular biology. In this paper we present miREE, a novel microRNA target prediction tool. miREE is an ensemble of two parts entailing complementary but integrated roles in the prediction. The Ab-Initio module leverages upon a genetic algorithmic approach to generate a set of candidate sites on the basis of their microRNA-mRNA duplex stability properties. Then, a Support Vector Machine (SVM) learning module evaluates the impact of microRNA recognition elements on the target gene. As a result the prediction takes into account information regarding both miRNA-target structural stability and accessibility. RESULTS: The proposed method significantly improves the state-of-the-art prediction tools in terms of accuracy with a better balance between specificity and sensitivity, as demonstrated by the experiments conducted on several large datasets across different species. miREE achieves this result by tackling two of the main challenges of current prediction tools: (1) The reduced number of false positives for the Ab-Initio part thanks to the integration of a machine learning module (2) the specificity of the machine learning part, obtained through an innovative technique for rich and representative negative records generation. The validation was conducted on experimental datasets where the miRNA:mRNA interactions had been obtained through (1) direct validation where even the binding site is provided, or through (2) indirect validation, based on gene expression variations obtained from high-throughput experiments where the specific interaction is not validated in detail and consequently the specific binding site is not provided. CONCLUSIONS: The coupling of two parts: a sensitive Ab-Initio module and a selective machine learning part capable of recognizing the false positives, leads to an improved balance between sensitivity and specificity. miREE obtains a reasonable trade-off between filtering false positives and identifying targets. miREE tool is available online at http://didattica-online.polito.it/eda/miREE/
format Online
Article
Text
id pubmed-3265527
institution National Center for Biotechnology Information
language English
publishDate 2011
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-32655272012-01-25 miREE: miRNA recognition elements ensemble Reyes-Herrera, Paula H Ficarra, Elisa Acquaviva, Andrea Macii, Enrico BMC Bioinformatics Research Article BACKGROUND: Computational methods for microRNA target prediction are a fundamental step to understand the miRNA role in gene regulation, a key process in molecular biology. In this paper we present miREE, a novel microRNA target prediction tool. miREE is an ensemble of two parts entailing complementary but integrated roles in the prediction. The Ab-Initio module leverages upon a genetic algorithmic approach to generate a set of candidate sites on the basis of their microRNA-mRNA duplex stability properties. Then, a Support Vector Machine (SVM) learning module evaluates the impact of microRNA recognition elements on the target gene. As a result the prediction takes into account information regarding both miRNA-target structural stability and accessibility. RESULTS: The proposed method significantly improves the state-of-the-art prediction tools in terms of accuracy with a better balance between specificity and sensitivity, as demonstrated by the experiments conducted on several large datasets across different species. miREE achieves this result by tackling two of the main challenges of current prediction tools: (1) The reduced number of false positives for the Ab-Initio part thanks to the integration of a machine learning module (2) the specificity of the machine learning part, obtained through an innovative technique for rich and representative negative records generation. The validation was conducted on experimental datasets where the miRNA:mRNA interactions had been obtained through (1) direct validation where even the binding site is provided, or through (2) indirect validation, based on gene expression variations obtained from high-throughput experiments where the specific interaction is not validated in detail and consequently the specific binding site is not provided. CONCLUSIONS: The coupling of two parts: a sensitive Ab-Initio module and a selective machine learning part capable of recognizing the false positives, leads to an improved balance between sensitivity and specificity. miREE obtains a reasonable trade-off between filtering false positives and identifying targets. miREE tool is available online at http://didattica-online.polito.it/eda/miREE/ BioMed Central 2011-11-24 /pmc/articles/PMC3265527/ /pubmed/22115078 http://dx.doi.org/10.1186/1471-2105-12-454 Text en Copyright ©2011 Reyes-Herrera et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Reyes-Herrera, Paula H
Ficarra, Elisa
Acquaviva, Andrea
Macii, Enrico
miREE: miRNA recognition elements ensemble
title miREE: miRNA recognition elements ensemble
title_full miREE: miRNA recognition elements ensemble
title_fullStr miREE: miRNA recognition elements ensemble
title_full_unstemmed miREE: miRNA recognition elements ensemble
title_short miREE: miRNA recognition elements ensemble
title_sort miree: mirna recognition elements ensemble
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3265527/
https://www.ncbi.nlm.nih.gov/pubmed/22115078
http://dx.doi.org/10.1186/1471-2105-12-454
work_keys_str_mv AT reyesherrerapaulah mireemirnarecognitionelementsensemble
AT ficarraelisa mireemirnarecognitionelementsensemble
AT acquavivaandrea mireemirnarecognitionelementsensemble
AT maciienrico mireemirnarecognitionelementsensemble