VIPR HMM: a hidden Markov model for detecting recombination with microbial detection microarrays
Author(s) -
Adam Allred,
Hilary Renshaw,
Scott C. Weaver,
Robert B. Tesh,
David Wang
Publication year - 2012
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/bts560
Subject(s) - hidden markov model , dna microarray , biology , recombinant dna , computational biology , microarray , genetics , genome , gene , computer science , artificial intelligence , gene expression
Current methods in diagnostic microbiology typically focus on the detection of a single genomic locus or protein in a candidate agent. The presence of the entire microbe is then inferred from this isolated result. Problematically, the presence of recombination in microbial genomes would go undetected unless other genomic loci or protein components were specifically assayed. Microarrays lend themselves well to the detection of multiple loci from a given microbe; furthermore, the inherent nature of microarrays facilitates highly parallel interrogation of multiple microbes. However, none of the existing methods for analyzing diagnostic microarray data has the capacity to specifically identify recombinant microbes. In previous work, we developed a novel algorithm, VIPR, for analyzing diagnostic microarray data.
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