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 clas-sification based on phylogenetic or statistical algo-rithms. 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, re-spectively). 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 recom-binant forms, a frequent feature of the HIV epidemic. Runtime comparisons showed that COMET was al-most as fast as USEARCH. This study demonstrates the advantages of alignment-free classification of vi-ral sequences, which feature high rates of variation, recombination and insertions/deletions. COMET is free to use via an online interface