title: E-Predict: a computational strategy for species identification based on observed DNA microarray hybridization patterns.
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.
authors.affiliation: Department of Biochemistry and Biophysics, University of California San Francisco, San Francisco, CA 94143, USA. anatoly@derisilab.ucsf.edu
authors.forename: Anatoly
authors.initials: A
authors.lastname: Urisman
authors.forename: Kael F
authors.initials: KF
authors.lastname: Fischer
authors.forename: Charles Y
authors.initials: CY
authors.lastname: Chiu
authors.forename: Amy L
authors.initials: AL
authors.lastname: Kistler
authors.forename: Shoshannah
authors.initials: S
authors.lastname: Beck
authors.forename: David
authors.initials: D
authors.lastname: Wang
authors.forename: Joseph L
authors.initials: JL
authors.lastname: DeRisi
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D004279:DNA, Viral / Q000235:genetics; D006367:HeLa Cells; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis; D010802:Phylogeny; D012107:Research Design; D012984:Software; D013045:Species Specificity
chemical_list: D004279:DNA, Viral