TAILIEUCHUNG - Báo cáo khoa học: Utilizing logical relationships in genomic data to decipher cellular processes

The wealth of available genomic data has spawned a corresponding interest in computational methods that can impart biological meaning and context to these experiments. Traditional computational methods have drawn rela-tionships between pairs of proteins or genes based on notions of equality or similarity between their patterns of occurrence or behavior. For exam-ple, two genes displaying similar variation in expression, over a number of experiments, may be predicted to be functionally related. | iFEBS Journal MINIREVIEW Utilizing logical relationships in genomic data to decipher cellular processes Peter M. Bowers1 2 Brian D. O Connor3 Shawn J. Cokus4 Einat Sprinzak2 Todd O. Yeates2 3 and David Eisenberg1 2 1 Howard Hughes Medical institute University of California Los Angeles CA USA 2 Institute for Genomics and Proteomics University of California Los Angeles CA USA 3 Department of Chemistry and Biochemistry University of California Los Angeles CA USA 4 Department of Mathematics University of California Los Angeles CA USA Keywords genomic data logic analysis microarray expression phylogenetic profile Correspondence D. Eisenberg Howard Hughes Medical Institute University of California Los Angeles Los Angeles CA 90095 USA Fax 1 310 206 39l4 E-mail david@ Note These authors contributed equally to this work Received 25 May 2005 revised 26 July 2005 accepted 2 August 2005 doi The wealth of available genomic data has spawned a corresponding interest in computational methods that can impart biological meaning and context to these experiments. Traditional computational methods have drawn relationships between pairs of proteins or genes based on notions of equality or similarity between their patterns of occurrence or behavior. For example two genes displaying similar variation in expression over a number of experiments may be predicted to be functionally related. We have introduced a natural extension of these approaches instead identifying logical relationships involving triplets of proteins. Triplets provide for various discrete kinds of logic relationships leading to detailed inferences about biological associations. For instance a protein C might be encoded within an organism if and only if two other proteins A and B are also both encoded within the organism thus suggesting that gene C is functionally related to genes A and B. The method has been applied fruitfully to both phylogenetic and microarray expression data

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