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RECOVIR: An application package to automatically identify some single stranded RNA viruses using capsid protein residues that uniquely distinguish among these viruses
Authors:Dianhui Zhu  George E Fox  Sugoto Chakravarty
Affiliation:(1) Dept of Computer Science, University of Houston, 4800 Calhoun Avenue, Houston, TX 77204-5001, USA;(2) Dept of Biology and Biochemistry, University of Houston, 4800 Calhoun Avenue, Houston, TX 77204-5001, USA;(3) Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA
Abstract:

Background  

Most single stranded RNA (ssRNA) viruses mutate rapidly to generate large number of strains having highly divergent capsid sequences. Accurate strain recognition in uncharacterized target capsid sequences is essential for epidemiology, diagnostics, and vaccine development. Strain recognition based on similarity scores between target sequences and sequences of homology matched reference strains is often time consuming and ambiguous. This is especially true if only partial target sequences are available or if different ssRNA virus families are jointly analyzed. In such cases, knowledge of residues that uniquely distinguish among known reference strains is critical for rapid and unambiguous strain identification. Conventional sequence comparisons are unable to identify such capsid residues due to high sequence divergence among the ssRNA virus reference strains. Consequently, automated general methods to reliably identify strains using strain distinguishing residues are not currently available.
Keywords:
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