Integrating alignment-based and alignment-free sequence similarity measures for biological sequence classification
Open Access
- 7 January 2015
- journal article
- research article
- Published by Oxford University Press (OUP) in Bioinformatics
- Vol. 31 (9), 1396-1404
- https://doi.org/10.1093/bioinformatics/btv006
Abstract
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.Keywords
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This publication has 30 references indexed in Scilit:
- Applying Shannon's information theory to bacterial and phage genomes and metagenomesScientific Reports, 2013
- PhymmBL expanded: confidence scores, custom databases, parallelization and moreNature Methods, 2011
- Taxonomic metagenome sequence assignment with structured output modelsNature Methods, 2011
- A fast, lock-free approach for efficient parallel counting of occurrences of k-mersBioinformatics, 2011
- NBC: the Naïve Bayes Classification tool webserver for taxonomic classification of metagenomic readsBioinformatics, 2010
- Phymm and PhymmBL: metagenomic phylogenetic classification with interpolated Markov modelsNature Methods, 2009
- Alignment-free genome comparison with feature frequency profiles (FFP) and optimal resolutionsProceedings of the National Academy of Sciences of the United States of America, 2009
- MEGAN analysis of metagenomic dataGenome Research, 2007
- Community structure and metabolism through reconstruction of microbial genomes from the environmentNature, 2004
- Gapped BLAST and PSI-BLAST: a new generation of protein database search programsNucleic Acids Research, 1997