E-Predict: a computational strategy for species identification based on observed DNA microarray hybridization patterns.
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.
Urisman
Anatoly
A
Department of Biochemistry and Biophysics, University of California San Francisco, San Francisco, CA 94143, USA. anatoly@derisilab.ucsf.edu
Fischer
Kael F
KF
Chiu
Charles Y
CY
Kistler
Amy L
AL
Beck
Shoshannah
S
Wang
David
D
DeRisi
Joseph L
JL
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
D004279:DNA, Viral