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

By Daniel Struck, G. Lawyer, A. Ternes, J. Schmit, Danielle Perez Bercoff. Nucleic Acids Research, 2014

380 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/49bfe3bfdff0cfd742e7f9248f455adb13481c21

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In Artificial Intelligence

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In Molecular Biology

  1. Andrzej Zielezinski
  2. W. Karłowski
  3. Jonas S. Almeida
  4. Susana Vinga
  5. Nicolas J.-B. Brunel

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