VIPR: A probabilistic algorithm for analysis of microbial detection microarrays

BMC Bioinformatics

Research Article

Robert B Tesh¹  Michael R Holbrook²  Kael F Fischer³  Adam F Allred⁴  David Wang⁴  Guang Wu⁴  Tuya Wulan⁴ 

[1] Department of Pathology, University of Texas Medical Branch, Galveston, Texas, USA;Department of Pathology, University of Texas Medical Branch, Galveston, Texas, USA;NIH Integrated Research Facility, Division of Clinical Medicine, 8200 Research Plaza, Fort Detrick, 21702, Frederick, MD, USA;Department of Pathology, University of Utah School of Medicine, Salt Lake City, Utah, USA;Departments of Molecular Microbiology and Pathology & Immunology, Washington University School of Medicine, St. Louis, Missouri, USA;

关键词: Dengue Virus;    Hemorrhagic Fever;    Probabilistic Algorithm;    Hemorrhagic Fever Virus;    Control Array;   

DOI  :  10.1186/1471-2105-11-384

 received in 2010-02-03, accepted in 2010-07-20,  发布年份 2010

来源: Springer

【 摘 要 】

BackgroundAll 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.ResultsTo specifically address this issue we have developed a novel interpretive algorithm, VIPR (V iral I dentification using a PR obabilistic 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. ConclusionsVIPR 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.

【 授权许可】

Unknown   
© Allred et al; licensee BioMed Central Ltd. 2010. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.