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

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 alig...

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Bibliographic Details
Published in:Nucleic acids research 2014-10, Vol.42 (18), p.e144-e144
Main Authors: Struck, Daniel, Lawyer, Glenn, Ternes, Anne-Marie, Schmit, Jean-Claude, Bercoff, Danielle Perez
Format: Article
Language:English
Subjects:
Algorithms
High-Throughput Nucleotide Sequencing - methods
HIV-1 - classification
HIV-1 - genetics
HIV-1 - isolation & purification
Humans
Life Sciences
Methods Online
Phylogeny
Software
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Summary: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.
ISSN:0305-1048
1362-4962
DOI:10.1093/nar/gku739