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Open Access Method

E-Predict: a computational strategy for species identification based on observed DNA microarray hybridization patterns

Anatoly Urisman12, Kael F Fischer1, Charles Y Chiu13, Amy L Kistler1, Shoshannah Beck1, David Wang4 and Joseph L DeRisi1*

Author Affiliations

1 Department of Biochemistry and Biophysics, University of California San Francisco, San Francisco, CA 94143, USA

2 Biomedical Sciences Graduate Program, University of California San Francisco, San Francisco, CA 94143, USA

3 Department of Infectious Diseases, University of California San Francisco, San Francisco, CA 94143, USA

4 Departments of Molecular Microbiology and Pathology and Immunology, Washington University School of Medicine, Saint Louis, MO 63110, USA

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Genome Biology 2005, 6:R78  doi:10.1186/gb-2005-6-9-r78

Published: 30 August 2005

Abstract

DNA microarrays may be used to identify microbial species present in environmental and clinical samples. However, automated tools for reliable species identification based on observed microarray hybridization patterns are lacking. We present an algorithm, E-Predict, for microarray-based species identification. E-Predict compares observed hybridization patterns with theoretical energy profiles representing different species. We demonstrate the application of the algorithm to viral detection in a set of clinical samples and discuss its relevance to other metagenomic applications.