tailieunhanh - Báo cáo y học: "E-Predict: a computational strategy for species identification based on observed DNA microarray hybridization patterns"

Tuyển tập các báo cáo nghiên cứu về y học được đăng trên tạp chí y học Wertheim cung cấp cho các bạn kiến thức về ngành y đề tài: E-Predict: a computational strategy for species identification based on observed DNA microarray hybridization patterns. | Method Open Access 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 and Joseph L DeRisi Addresses Department of Biochemistry and Biophysics University of California San Francisco San Francisco CA 94143 USA. Biomedical Sciences Graduate Program University of California San Francisco San Francisco CA 94143 USA. Department of Infectious Diseases University of California San Francisco San Francisco CA 94143 USA. Departments of Molecular Microbiology and Pathology and Immunology Washington University School of Medicine Saint Louis MO 63110 USA. Correspondence Joseph L DeRisi. E-mail joe@ Published 30 August 2005 Genome Biology 2005 6 R78 doi l86 gb-2005-6-9-r78 The electronic version of this article is the complete one and can be found online at http 2005 6 9 R78 Received 26 April 2005 Revised 23 June 2005 Accepted 26 July 2005 2005 Urisman et al. licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License http licenses by which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. 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 microarraybased 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. Background Metagenomics an emerging field of biology utilizes .

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