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Bioinformatics Advance Access published online on April 27, 2006
Bioinformatics, doi:10.1093/bioinformatics/btl162
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© The Author (2006). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received January 23, 2006
Revised April 16, 2006
Accepted April 23, 2006

Article

Identification of humoral immune responses in protein microarrays using DNA microarray data analysis techniques

Suman Sundaresh 1, Denise L. Doolan 2, Siddiqua Hirst 3, Yunxiang Mu 3, Berkay Unal 3, D. Huw Davies 3, Philip L. Felgner 3, and Pierre Baldi 1 *

1 School of Information and Computer Sciences, University of California, Irvine, CA; Institute for Genomics and Bioinformatics, University of California, Irvine, CA
2 Naval Medical Research Center, Silver Spring, MD; Department of Molecular Microbiology and Immunology, School of Hygiene and Public Health, Johns Hopkins University, Baltimore, MD
3 Center for Virus Research, University of California, Irvine, CA

* To whom correspondence should be addressed.
Pierre Baldi, E-mail: pfbaldi{at}uci.edu


  Abstract

Motivation: We present a study of antigen expression signals from a newly developed high throughput protein microarray technique. These signals are a measure of antibody-antigen binding activity and provide a basis for understanding humoral immune responses to various infectious agents and supporting vaccine and diagnostic development.

Results: We investigate the characteristic of these expression profiles and show that noise models, normalization, variance estimation and differential expression analysis techniques developed in the context of DNA microarray analysis can be adapted and applied to these protein arrays. Using a high dimensional dataset containing measurements of expression profiles of antibody reactivity against each protein (295 antigens and 9 controls) in 42 malaria (Plasmodium falciparum) protein arrays derived from 22 donors with various clinical presentations of malaria, we present a methodology for the analysis and identification of significantly expressed antigens targeted by immune responses for individual sera, groups of sera and across stages of infection. We also conduct a short study highlighting the top immunoreactive antigens where we identify three novel high priority antigens for future evaluation.

Availability: All software programs (in R) used for the analysis described in this paper are freely available for academic purposes at www.igb.uci.edu/servers/servers.html.

Associate Editor: David Rocke
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