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Integrating alignment-based and alignment-free sequence similarity measures for biological sequence classification
Motivation: Alignment-based sequence similarity searches, while accurate for some type of sequences, can produce incorrect results when used on more divergent but functionally related sequences that have undergone the sequence rearrangements observed in many bacterial and viral genomes. Here, we pro...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4410667/ https://www.ncbi.nlm.nih.gov/pubmed/25573913 http://dx.doi.org/10.1093/bioinformatics/btv006 |
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author | Borozan, Ivan Watt, Stuart Ferretti, Vincent |
author_facet | Borozan, Ivan Watt, Stuart Ferretti, Vincent |
author_sort | Borozan, Ivan |
collection | PubMed |
description | Motivation: Alignment-based sequence similarity searches, while accurate for some type of sequences, can produce incorrect results when used on more divergent but functionally related sequences that have undergone the sequence rearrangements observed in many bacterial and viral genomes. Here, we propose a classification model that exploits the complementary nature of alignment-based and alignment-free similarity measures with the aim to improve the accuracy with which DNA and protein sequences are characterized. Results: Our model classifies sequences using a combined sequence similarity score calculated by adaptively weighting the contribution of different sequence similarity measures. Weights are determined independently for each sequence in the test set and reflect the discriminatory ability of individual similarity measures in the training set. Because the similarity between some sequences is determined more accurately with one type of measure rather than another, our classifier allows different sets of weights to be associated with different sequences. Using five different similarity measures, we show that our model significantly improves the classification accuracy over the current composition- and alignment-based models, when predicting the taxonomic lineage for both short viral sequence fragments and complete viral sequences. We also show that our model can be used effectively for the classification of reads from a real metagenome dataset as well as protein sequences. Availability and implementation: All the datasets and the code used in this study are freely available at https://collaborators.oicr.on.ca/vferretti/borozan_csss/csss.html. Contact: ivan.borozan@gmail.com Supplementary information: Supplementary data are available at Bioinformatics online. |
format | Online Article Text |
id | pubmed-4410667 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-44106672015-04-30 Integrating alignment-based and alignment-free sequence similarity measures for biological sequence classification Borozan, Ivan Watt, Stuart Ferretti, Vincent Bioinformatics Original Papers Motivation: Alignment-based sequence similarity searches, while accurate for some type of sequences, can produce incorrect results when used on more divergent but functionally related sequences that have undergone the sequence rearrangements observed in many bacterial and viral genomes. Here, we propose a classification model that exploits the complementary nature of alignment-based and alignment-free similarity measures with the aim to improve the accuracy with which DNA and protein sequences are characterized. Results: Our model classifies sequences using a combined sequence similarity score calculated by adaptively weighting the contribution of different sequence similarity measures. Weights are determined independently for each sequence in the test set and reflect the discriminatory ability of individual similarity measures in the training set. Because the similarity between some sequences is determined more accurately with one type of measure rather than another, our classifier allows different sets of weights to be associated with different sequences. Using five different similarity measures, we show that our model significantly improves the classification accuracy over the current composition- and alignment-based models, when predicting the taxonomic lineage for both short viral sequence fragments and complete viral sequences. We also show that our model can be used effectively for the classification of reads from a real metagenome dataset as well as protein sequences. Availability and implementation: All the datasets and the code used in this study are freely available at https://collaborators.oicr.on.ca/vferretti/borozan_csss/csss.html. Contact: ivan.borozan@gmail.com Supplementary information: Supplementary data are available at Bioinformatics online. Oxford University Press 2015-05-01 2015-01-07 /pmc/articles/PMC4410667/ /pubmed/25573913 http://dx.doi.org/10.1093/bioinformatics/btv006 Text en © The Author 2015. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Original Papers Borozan, Ivan Watt, Stuart Ferretti, Vincent Integrating alignment-based and alignment-free sequence similarity measures for biological sequence classification |
title | Integrating alignment-based and alignment-free sequence similarity measures for biological sequence classification |
title_full | Integrating alignment-based and alignment-free sequence similarity measures for biological sequence classification |
title_fullStr | Integrating alignment-based and alignment-free sequence similarity measures for biological sequence classification |
title_full_unstemmed | Integrating alignment-based and alignment-free sequence similarity measures for biological sequence classification |
title_short | Integrating alignment-based and alignment-free sequence similarity measures for biological sequence classification |
title_sort | integrating alignment-based and alignment-free sequence similarity measures for biological sequence classification |
topic | Original Papers |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4410667/ https://www.ncbi.nlm.nih.gov/pubmed/25573913 http://dx.doi.org/10.1093/bioinformatics/btv006 |
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