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| Plant Computational Biology (Heiko Schoof) |
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Group Schoof
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Homepage of the Plant Computational Biology group
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Research topics of the Plant Computational Biology group
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1. Biological data integration, knowledge representation and database interoperability on the basis of internet technologies like web services or semantic web;
2. Prediction and analysis of regulatory elements in the untranslated regions of plant mRNAs using statistical, machine learning and phylogenetic tools;
3. (comparative) genome analysis, e.g. in the scope of the tomato genome projects, where the group is involved in annotation of new genomic sequences in an international consortium.
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1. Several international projects are tackling the problem of ensuring availability of comprehensive, current and integrated genomics data. One approach for data integration is data warehousing, but a distributed network of interconnected databases is more flexible, robust and scalable.  Webservices are being used in the "PlaNet" and biomoby projects to achieve database interoperability. Our group will contribute to the development of a standard and apply it to support data mining projects, also in collaboration with other groups at MPIZ. One application is to provide a single portal through which a user can query current information from any number of different remote databases that are connected through web services. Another application is building analysis workflows, where a number of query or processing steps can be combined into a pipeline and executed automatically (see Figure 1). Using Taverna , hundreds of web services can be combined. Our group will provide data and calculation services within this networked platform and develop user software to facilitate bioinformatic analysis. The development of standardized data models and formats and the utilization of semantic web technology to semantically describe biological knowledge so that it can be interpreted by computers enhance and complement these efforts.
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Figure 1: Taverna workflow to compare sequence and keyword searches in an Arabidopsis thaliana genome database.
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2. Preliminary experiments to detect conserved sequence motifs in the untranslated regions of Arabidopsis thaliana mRNAs using sequence analysis and machine learning uncovered significant correlations between conserved motifs and RNA structure as well as microRNA binding sites. These results need to be evaluated systematically and complemented by the analysis of conservation in related genomes.
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3. Within the tomato genome project we will participate in the annotation of genome sequences within international consortia by implementing automatic analysis procedures. Comparative genome analysis will allow insights into genome evolution, duplications, gene family expansions, non-coding functional sequences and synteny. This project is interlinked with (1), data integration and knowledge representation, through the planned work on phenotype databases and interconnecting of individual project databases.
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© 2011, Max Planck Institute for Plant Breeding Research, Cologne |
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