E-Predict: a computational strategy for species identification based on observed DNA microarray hybridization patterns |
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| Authors: | Anatoly?Urisman,Kael?F?Fischer,Charles?Y?Chiu,Amy?L?Kistler,Shoshannah?Beck,David?Wang,Joseph?L?DeRisi mailto:joe@derisilab.ucsf.edu" title=" joe@derisilab.ucsf.edu" itemprop=" email" data-track=" click" data-track-action=" Email author" data-track-label=" " >Email author |
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| Affiliation: | (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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| 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. |
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