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Biological Data Integration

Biological Data Integration. July 22, 2003 GTL Data and Tools Workshop Gaithersburg, MD Cathy H. Wu, Ph.D. Professor of Biochemistry & Molecular Biology Director, Protein Information Resource Georgetown University Medical Center .

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Biological Data Integration

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  1. Biological Data Integration July 22, 2003 GTL Data and Tools Workshop Gaithersburg, MD Cathy H. Wu, Ph.D. Professor of Biochemistry & Molecular Biology Director, Protein Information Resource Georgetown University Medical Center

  2. Three types of ATR (EC 2.5.1.17): PduO type (SF036411, SF015651) EutT type (SF012294) CobA type (SF015617) AdoCbl Cofactor Biosynthesis 2.5.1.17 AdoCbl In Archaeoglobus fulgidus, AF1290 gene (SF015651 member) co-occurs with methylmalonyl CoA mutase (EC 5.4.99.2) gene supports AdoCbl-dependent diol/glycerol dehydratases (EC 4.2.1.28) (Salmonella PduO is experimentally characterized, many predicted to support the same enzyme based on gene context) AF1288 (EC5.4.99.2) AF1290 (EC2.5.1.17) Leads to prediction that ATR of the PduO type can support AdoCbl-dependent methylmalonyl CoA mutase, therefore corresponds to the cblB complementation group of the methylmalonic aciduria disorder AdoCbl Prediction is experimentally verified, human ATR cloned by complementation of ATR-deficiend Salmonella mutant AdoCbl Propanediol Utilization Propionyl-CoA Metabolism

  3. Bioinformatics System Requirements for Function and Pathway Discovery • Data Integration: coupling homology search results with integrative biology information (genome context, protein fusions, phylogenetic profiles, pathways, protein interactions, complexes, gene/protein expression) • Associative Analysis: associating complete genomes with phylogenies, pathways, and networks • Evidence Attribution: attributing sources and strengths of evidence • User Interactivity: allowing interactive, iterative, and custom-tailored analyses

  4. Biological Data Integration • Challenge • Voluminous, Complex, Dynamic, Heterogeneous, Distributed • Issues • Nomenclature and Ontology • Distribution Formats • Annotation Errors and Error Propagation • Approaches (UniProt/iProClass) • Standardized Nomenclature (Protein Names) • Controlled Vocabulary (Features, Keywords) • Accepted Nomenclature/Ontologies (EC, GO, NCBI Taxonomy) • Common Distribution Formats (XML/DTD, MySQL/DB Schema, Object Models) • Evidence Attribution • Family Classification and Rule-Based Annotation

  5. Evidence Attribution • Sources and Strengths of Evidence • Experimentally Verified vs. Computationally Predicted • Retrospective Literature Survey Classification-Driven, Rule-Based Annotation • Systematic Detection and Correction of Annotation Errors • Consistent Annotation of Protein Names, Features, Keywords/GO Terms

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