title: Integrating alignment-based and alignment-free sequence similarity measures for biological sequence classification.
abstract: BACKGROUND
  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.
  
  
  METHODS
  All the datasets and the code used in this study are freely available at https://collaborators.oicr.on.ca/vferretti/borozan_csss/csss.html.
  
  
  BACKGROUND
  ivan.borozan@gmail.com
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Department of Informatics and Bio-computing, Ontario Institute for Cancer Research, MaRS Centre, South Tower, 101 College Street, Suite 800, Toronto, Ontario, Canada.
authors.forename: Ivan
authors.initials: I
authors.lastname: Borozan
authors.affiliation: Department of Informatics and Bio-computing, Ontario Institute for Cancer Research, MaRS Centre, South Tower, 101 College Street, Suite 800, Toronto, Ontario, Canada.
authors.forename: Stuart
authors.initials: S
authors.lastname: Watt
authors.affiliation: Department of Informatics and Bio-computing, Ontario Institute for Cancer Research, MaRS Centre, South Tower, 101 College Street, Suite 800, Toronto, Ontario, Canada.
authors.forename: Vincent
authors.initials: V
authors.lastname: Ferretti
mesh_terms: D000465:Algorithms; D002965:Classification / Q000379:methods; D004279:DNA, Viral; D056186:Metagenomics; D008962:Models, Theoretical; D016415:Sequence Alignment; D017422:Sequence Analysis, DNA / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods*; D014780:Viruses / Q000145:classification
chemical_list: D004279:DNA, Viral