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Open Access Highly Accessed Research article

Detection of viral sequence fragments of HIV-1 subfamilies yet unknown

Thomas Unterthiner1, Anne-Kathrin Schultz1, Jan Bulla2, Burkhard Morgenstern1, Mario Stanke3 and Ingo Bulla13*

Author Affiliations

1 Institute of Microbiology and Genetics, University of Göttingen, Goldschmidtstr. 1, 37077 Göttingen, Germany

2 LMNO, Université de Caen, CNRS UMR 6139, 14032 Caen Cedex, France

3 Institut für Mathematik und Informatik, Walther-Rathenau-Straße 47, 17487 Greifswald, Germany

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BMC Bioinformatics 2011, 12:93  doi:10.1186/1471-2105-12-93

Published: 11 April 2011

Abstract

Background

Methods of determining whether or not any particular HIV-1 sequence stems - completely or in part - from some unknown HIV-1 subtype are important for the design of vaccines and molecular detection systems, as well as for epidemiological monitoring. Nevertheless, a single algorithm only, the Branching Index (BI), has been developed for this task so far. Moving along the genome of a query sequence in a sliding window, the BI computes a ratio quantifying how closely the query sequence clusters with a subtype clade. In its current version, however, the BI does not provide predicted boundaries of unknown fragments.

Results

We have developed Unknown Subtype Finder (USF), an algorithm based on a probabilistic model, which automatically determines which parts of an input sequence originate from a subtype yet unknown. The underlying model is based on a simple profile hidden Markov model (pHMM) for each known subtype and an additional pHMM for an unknown subtype. The emission probabilities of the latter are estimated using the emission frequencies of the known subtypes by means of a (position-wise) probabilistic model for the emergence of new subtypes. We have applied USF to SIV and HIV-1 sequences formerly classified as having emerged from an unknown subtype. Moreover, we have evaluated its performance on artificial HIV-1 recombinants and non-recombinant HIV-1 sequences. The results have been compared with the corresponding results of the BI.

Conclusions

Our results demonstrate that USF is suitable for detecting segments in HIV-1 sequences stemming from yet unknown subtypes. Comparing USF with the BI shows that our algorithm performs as good as the BI or better.