Help Guide Disclaimer Contact us Login
  Advanced SearchBrowse




Journal Article

COMET: Adaptive Context-based Modeling for Ultrafast HIV-1 Subtype Identification


Lawyer,  Glenn
Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society;


(Any fulltext)

Fulltext (public)
There are no public fulltexts available
Supplementary Material (public)
There is no public supplementary material available

Struck, D., Lawyer, G., Ternes, A.-M., Schmit, J.-C., & Perez Bercoff, D. (2014). COMET: Adaptive Context-based Modeling for Ultrafast HIV-1 Subtype Identification. Nucleic Acids Research, 42(18): e144. doi:10.1093/nar/gku739.

Cite as:
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.