Classification of HIV-1 sequences using profile Hidden Markov Models.

ABSTRACT: Accurate classification of HIV-1 subtypes is essential for studying the dynamic spatial distribution pattern of HIV-1 subtypes and also for developing effective methods of treatment that can be targeted to attack specific subtypes. We propose a classification method based on profile Hidden Markov Model that can accurately identify an unknown strain. We show that a standard method that relies on the construction of a positive training set only, to capture unique features associated with a particular subtype, can accurately classify sequences belonging to all subtypes except B and D. We point out the drawbacks of the standard method; namely, an arbitrary choice of threshold to distinguish between true positives and true negatives, and the inability to discriminate between closely related sub

SUBMITTER: Dwivedi SK 

PROVIDER: S-EPMC3356369 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature