Open Access Research article

VIPR: A probabilistic algorithm for analysis of microbial detection microarrays

Adam F Allred1, Guang Wu1, Tuya Wulan1, Kael F Fischer2, Michael R Holbrook34, Robert B Tesh3 and David Wang1*

Author Affiliations

1 Departments of Molecular Microbiology and Pathology & Immunology, Washington University School of Medicine, St. Louis, Missouri USA

2 Department of Pathology, University of Utah School of Medicine, Salt Lake City, Utah USA

3 Department of Pathology, University of Texas Medical Branch, Galveston, Texas USA

4 NIH Integrated Research Facility, Division of Clinical Medicine, 8200 Research Plaza, Fort Detrick, Frederick, MD 21702

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BMC Bioinformatics 2010, 11:384  doi:10.1186/1471-2105-11-384

Published: 20 July 2010



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. Conclusions

VIPR outperformed previously described algorithms for this dataset. The VIPR algorithm has potential to be broadly applicable to clinical diagnostic settings, wherein positive controls are typically readily available for generation of training data.