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E-Predict Training Data Set and Examples

ABSTRACT: This series includes a 32-array training dataset used to evaluate E-Predict normalization and similarity metric parameters as well as 13 microarrays used as examples in (Urisman, et. al 2005). Training data set includes 15 independent HeLa RNAhybridizations (microarrays 1-15), 10 independent nasal lavage samples positive for Respiratory Syncytial virus (microarrays 16-25), and 7 independent nasal lavage samples positive for Influenza A virus (microarrays 26-32). Examples iclude a serum sample positive for Hepatitis B virus (microarray 33), a nasal lavage sample positive for both Influenza A virus and Respiratory Syncytial virus (microarray 34), and culture samples of 11 distinct Human Rhinovirus serotypes (microarrays 35-45). Keywords = virus detection, E-Predict, species identification, metagenomics

ORGANISM(S): Viruses

SUBMITTER: Joseph DeRisi

PROVIDER: E-GEOD-2228 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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

Urisman Anatoly A Fischer Kael F KF Chiu Charles Y CY Kistler Amy L AL Beck Shoshannah S Wang David D DeRisi Joseph L JL

Genome biology 20050830 9

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 ...[more]

PMID: 16168085

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