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Stepwise approach for combining many sources of evidence for site-recognition in genomic sequences
BACKGROUND: Recognizing the different functional parts of genes, such as promoters, translation initiation sites, donors, acceptors and stop codons, is a fundamental task of many current studies in Bioinformatics. Currently, the most successful methods use powerful classifiers, such as support vecto...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4779560/ https://www.ncbi.nlm.nih.gov/pubmed/26945666 http://dx.doi.org/10.1186/s12859-016-0968-y |
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author | Pérez-Rodríguez, Javier García-Pedrajas, Nicolás |
author_facet | Pérez-Rodríguez, Javier García-Pedrajas, Nicolás |
author_sort | Pérez-Rodríguez, Javier |
collection | PubMed |
description | BACKGROUND: Recognizing the different functional parts of genes, such as promoters, translation initiation sites, donors, acceptors and stop codons, is a fundamental task of many current studies in Bioinformatics. Currently, the most successful methods use powerful classifiers, such as support vector machines with various string kernels. However, with the rapid evolution of our ability to collect genomic information, it has been shown that combining many sources of evidence is fundamental to the success of any recognition task. With the advent of next-generation sequencing, the number of available genomes is increasing very rapidly. Thus, methods for making use of such large amounts of information are needed. RESULTS: In this paper, we present a methodology for combining tens or even hundreds of different classifiers for an improved performance. Our approach can include almost a limitless number of sources of evidence. We can use the evidence for the prediction of sites in a certain species, such as human, or other species as needed. This approach can be used for any of the functional recognition tasks cited above. However, to provide the necessary focus, we have tested our approach in two functional recognition tasks: translation initiation site and stop codon recognition. We have used the entire human genome as a target and another 20 species as sources of evidence and tested our method on five different human chromosomes. The proposed method achieves better accuracy than the best state-of-the-art method both in terms of the geometric mean of the specificity and sensitivity and the area under the receiver operating characteristic and precision recall curves. Furthermore, our approach shows a more principled way for selecting the best genomes to be combined for a given recognition task. CONCLUSIONS: Our approach has proven to be a powerful tool for improving the performance of functional site recognition, and it is a useful method for combining many sources of evidence for any recognition task in Bioinformatics. The results also show that the common approach of heuristically choosing the species to be used as source of evidence can be improved because the best combinations of genomes for recognition were those not usually selected. Although the experiments were performed for translation initiation site and stop codon recognition, any other recognition task may benefit from our methodology. |
format | Online Article Text |
id | pubmed-4779560 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-47795602016-03-07 Stepwise approach for combining many sources of evidence for site-recognition in genomic sequences Pérez-Rodríguez, Javier García-Pedrajas, Nicolás BMC Bioinformatics Research Article BACKGROUND: Recognizing the different functional parts of genes, such as promoters, translation initiation sites, donors, acceptors and stop codons, is a fundamental task of many current studies in Bioinformatics. Currently, the most successful methods use powerful classifiers, such as support vector machines with various string kernels. However, with the rapid evolution of our ability to collect genomic information, it has been shown that combining many sources of evidence is fundamental to the success of any recognition task. With the advent of next-generation sequencing, the number of available genomes is increasing very rapidly. Thus, methods for making use of such large amounts of information are needed. RESULTS: In this paper, we present a methodology for combining tens or even hundreds of different classifiers for an improved performance. Our approach can include almost a limitless number of sources of evidence. We can use the evidence for the prediction of sites in a certain species, such as human, or other species as needed. This approach can be used for any of the functional recognition tasks cited above. However, to provide the necessary focus, we have tested our approach in two functional recognition tasks: translation initiation site and stop codon recognition. We have used the entire human genome as a target and another 20 species as sources of evidence and tested our method on five different human chromosomes. The proposed method achieves better accuracy than the best state-of-the-art method both in terms of the geometric mean of the specificity and sensitivity and the area under the receiver operating characteristic and precision recall curves. Furthermore, our approach shows a more principled way for selecting the best genomes to be combined for a given recognition task. CONCLUSIONS: Our approach has proven to be a powerful tool for improving the performance of functional site recognition, and it is a useful method for combining many sources of evidence for any recognition task in Bioinformatics. The results also show that the common approach of heuristically choosing the species to be used as source of evidence can be improved because the best combinations of genomes for recognition were those not usually selected. Although the experiments were performed for translation initiation site and stop codon recognition, any other recognition task may benefit from our methodology. BioMed Central 2016-03-05 /pmc/articles/PMC4779560/ /pubmed/26945666 http://dx.doi.org/10.1186/s12859-016-0968-y Text en © Pérez-Rodríguez and García-Pedrajas. 2016 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Pérez-Rodríguez, Javier García-Pedrajas, Nicolás Stepwise approach for combining many sources of evidence for site-recognition in genomic sequences |
title | Stepwise approach for combining many sources of evidence for site-recognition in genomic sequences |
title_full | Stepwise approach for combining many sources of evidence for site-recognition in genomic sequences |
title_fullStr | Stepwise approach for combining many sources of evidence for site-recognition in genomic sequences |
title_full_unstemmed | Stepwise approach for combining many sources of evidence for site-recognition in genomic sequences |
title_short | Stepwise approach for combining many sources of evidence for site-recognition in genomic sequences |
title_sort | stepwise approach for combining many sources of evidence for site-recognition in genomic sequences |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4779560/ https://www.ncbi.nlm.nih.gov/pubmed/26945666 http://dx.doi.org/10.1186/s12859-016-0968-y |
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