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A Global Approach to Comparative Genomics: Comparison of Functional Annotation over the Taxonomic Tree

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons45392

Schlicker,  Andreas
Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society;
International Max Planck Research School, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons44341

Domingues,  Francisco S.
Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons44907

Lengauer,  Thomas
Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society;

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Zitation

Schlicker, A. (2005). A Global Approach to Comparative Genomics: Comparison of Functional Annotation over the Taxonomic Tree. Master Thesis, Universität des Saarlandes, Saarbrücken.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-000F-258D-A
Zusammenfassung
Genome sequencing projects produce large amounts of data that are stored in sequence databases. Entries in these databases are annotated using the results of different experiments and computational methods. These methods usually rely on homology detection based on sequence similarity searches. Gene Ontology (GO) provides a standard vocabulary of functional terms, and allows a coherent annotation of gene products. These annotations can be used as a basis for new methods that compare gene products on the basis of their molecular function and biological role. In this thesis, we present a new approach for integrating the species taxonomy, protein family classifications and GO annotations. We implemented a database and a client application, GOTaxExplorer, that can be used to perform queries with a simplified language and to process and visualize the results. It allows to compare different taxonomic groups regarding the protein families or the protein functions associated with the different genomes. We developed a method for comparing GO annotations which includes a measure of functional similarity between gene products. The method was able to find functional relationships even if the proteins show no significant sequence similarity. We provide results for different application scenarios, in particular for the identification of new drug targets.