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 iden...

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Detalles Bibliográficos
Autores principales: Urisman, Anatoly, Fischer, Kael F, Chiu, Charles Y, Kistler, Amy L, Beck, Shoshannah, Wang, David, DeRisi, Joseph L
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2005
Materias:
Method
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1242213/
https://www.ncbi.nlm.nih.gov/pubmed/16168085
http://dx.doi.org/10.1186/gb-2005-6-9-r78
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author Urisman, Anatoly
Fischer, Kael F
Chiu, Charles Y
Kistler, Amy L
Beck, Shoshannah
Wang, David
DeRisi, Joseph L
author_facet Urisman, Anatoly
Fischer, Kael F
Chiu, Charles Y
Kistler, Amy L
Beck, Shoshannah
Wang, David
DeRisi, Joseph L
author_sort Urisman, Anatoly
collection PubMed
description 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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spelling pubmed-12422132005-10-06 E-Predict: a computational strategy for species identification based on observed DNA microarray hybridization patterns Urisman, Anatoly Fischer, Kael F Chiu, Charles Y Kistler, Amy L Beck, Shoshannah Wang, David DeRisi, Joseph L Genome Biol Method 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. BioMed Central 2005 2005-08-30 /pmc/articles/PMC1242213/ /pubmed/16168085 http://dx.doi.org/10.1186/gb-2005-6-9-r78 Text en Copyright © 2005 Urisman et al.; licensee BioMed Central Ltd.
spellingShingle Method
Urisman, Anatoly
Fischer, Kael F
Chiu, Charles Y
Kistler, Amy L
Beck, Shoshannah
Wang, David
DeRisi, Joseph L
E-Predict: a computational strategy for species identification based on observed DNA microarray hybridization patterns
title E-Predict: a computational strategy for species identification based on observed DNA microarray hybridization patterns
title_full E-Predict: a computational strategy for species identification based on observed DNA microarray hybridization patterns
title_fullStr E-Predict: a computational strategy for species identification based on observed DNA microarray hybridization patterns
title_full_unstemmed E-Predict: a computational strategy for species identification based on observed DNA microarray hybridization patterns
title_short E-Predict: a computational strategy for species identification based on observed DNA microarray hybridization patterns
title_sort e-predict: a computational strategy for species identification based on observed dna microarray hybridization patterns
topic Method
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1242213/
https://www.ncbi.nlm.nih.gov/pubmed/16168085
http://dx.doi.org/10.1186/gb-2005-6-9-r78
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