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Bioinformatics 2007 23(13):i508-i518; doi:10.1093/bioinformatics/btm207
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© 2007 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

From protein microarrays to diagnostic antigen discovery: a study of the pathogen Francisella tularensis

Suman Sundaresh 1,2, Arlo Randall 1,2, Berkay Unal 3, Jeannine M. Petersen 4, John T. Belisle 5, M. Gill Hartley 6, Melanie Duffield 6, Richard W. Titball 6, D. Huw Davies 3, Philip L. Felgner 3 and Pierre Baldi 1,2,*

1School of Information and Computer Sciences, 2Institute for Genomics and Bioinformatics, 3Center for Virus Research, University of California, Irvine, CA, 4Centers for Disease Control and Prevention, 5Mycobacteria Research Laboratories, Department of Microbiology, Immunology, and Pathology, Colorado State University, Fort Collins, CO, USA and 6Defence Science and Technology Laboratory, Porton Down, UK

*To whom correspondence should be addressed.


  Abstract

Motivation: An important application of protein microarray data analysis is identifying a serodiagnostic antigen set that can reliably detect patterns and classify antigen expression profiles. This work addresses this problem using antibody responses to protein markers measured by a novel high-throughput microarray technology. The findings from this study have direct relevance to rapid, broad-based diagnostic and vaccine development.

Results: Protein microarray chips are probed with sera from individuals infected with the bacteria Francisella tularensis, a category A biodefense pathogen. A two-step approach to the diagnostic process is presented (1) feature (antigen) selection and (2) classification using antigen response measurements obtained from F.tularensis microarrays (244 antigens, 46 infected and 54 healthy human sera measurements). To select antigens, a ranking scheme based on the identification of significant immune responses and differential expression analysis is described. Classification methods including k-nearest neighbors, support vector machines (SVM) and k-Means clustering are applied to training data using selected antigen sets of various sizes. SVM based models yield prediction accuracy rates in the range of ~90% on validation data, when antigen set sizes are between 25 and 50. These results strongly indicate that the top-ranked antigens can be considered high-priority candidates for diagnostic development.

Availability: All software programs are written in R and available at http://www.igb.uci.edu/index.php?page=tools and at http://www.r-project.org

Contact: pfbaldi{at}uci.edu

Supplementary information: Supplementary data are available at Bioinformatics online.


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