VIPR: A probabilistic algorithm for analysis of microbial detection microarrays.

BMC Bioinformatics
Adam F AllredDavid Wang

Abstract

All infectious disease oriented clinical diagnostic assays in use today focus on detecting the presence of a single, well defined target agent or a set of agents. In recent years, microarray-based diagnostics have been developed that greatly facilitate the highly parallel detection of multiple microbes that may be present in a given clinical specimen. While several algorithms have been described for interpretation of diagnostic microarrays, none of the existing approaches is capable of incorporating training data generated from positive control samples to improve performance. To specifically address this issue we have developed a novel interpretive algorithm, VIPR (Viral Identification using a PRobabilistic algorithm), which uses Bayesian inference to capitalize on empirical training data to optimize detection sensitivity. To illustrate this approach, we have focused on the detection of viruses that cause hemorrhagic fever (HF) using a custom HF-virus microarray. VIPR was used to analyze 110 empirical microarray hybridizations generated from 33 distinct virus species. An accuracy of 94% was achieved as measured by leave-one-out cross validation. VIPR outperformed previously described algorithms for this dataset. The VIPR algori...Continue Reading

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Citations

Sep 21, 2011·Briefings in Functional Genomics·Kevin S McLoughlin
Jun 4, 2013·Current Opinion in Microbiology·Charles Y Chiu

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Datasets Mentioned

BETA
GSM534862
GSM534971

Methods Mentioned

BETA
Chip

Software Mentioned

OligoArraySelector
Predict
VIPR
GenePix Pro
DetectiV
PhyloDetect
BLASTN

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