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Nucleic Acids Res. 2014 Oct;42(18):e144. doi: 10.1093/nar/gku739. Epub 2014 Aug 12.

COMET: adaptive context-based modeling for ultrafast HIV-1 subtype identification.

Author information

1
Laboratory of Retrovirology, CRP-Santé, 84, Val Fleuri, L-1526, Luxembourg daniel.struck@crp-sante.lu.
2
Department of Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, Campus E1 4, 66123 Saarbrücken, Germany.
3
Laboratory of Retrovirology, CRP-Santé, 84, Val Fleuri, L-1526, Luxembourg.

Abstract

Viral sequence classification has wide applications in clinical, epidemiological, structural and functional categorization studies. Most existing approaches rely on an initial alignment step followed by classification based on phylogenetic or statistical algorithms. Here we present an ultrafast alignment-free subtyping tool for human immunodeficiency virus type one (HIV-1) adapted from Prediction by Partial Matching compression. This tool, named COMET, was compared to the widely used phylogeny-based REGA and SCUEAL tools using synthetic and clinical HIV data sets (1,090,698 and 10,625 sequences, respectively). COMET's sensitivity and specificity were comparable to or higher than the two other subtyping tools on both data sets for known subtypes. COMET also excelled in detecting and identifying new recombinant forms, a frequent feature of the HIV epidemic. Runtime comparisons showed that COMET was almost as fast as USEARCH. This study demonstrates the advantages of alignment-free classification of viral sequences, which feature high rates of variation, recombination and insertions/deletions. COMET is free to use via an online interface.

PMID:
25120265
PMCID:
PMC4191385
DOI:
10.1093/nar/gku739
[Indexed for MEDLINE]
Free PMC Article

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