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

Anatoly Urisman, Kael F. Fischer, Charles Y. Chiu, Amy L. Kistler, Shoshannah Beck, David Wang, Joseph L. DeRisi

Research output: Contribution to journalArticle

61 Scopus citations

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.

Original languageEnglish
Pages (from-to)R78
JournalGenome biology
Volume6
Issue number9
StatePublished - 2005
Externally publishedYes

Fingerprint Dive into the research topics of 'E-Predict: a computational strategy for species identification based on observed DNA microarray hybridization patterns.'. Together they form a unique fingerprint.

  • Cite this

    Urisman, A., Fischer, K. F., Chiu, C. Y., Kistler, A. L., Beck, S., Wang, D., & DeRisi, J. L. (2005). E-Predict: a computational strategy for species identification based on observed DNA microarray hybridization patterns. Genome biology, 6(9), R78.