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Integration of Heterogeneous Informations Sources for Proteomics and Transcriptomics Steffen Möller University of Rostock Proteome Center.

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Presentation on theme: "Integration of Heterogeneous Informations Sources for Proteomics and Transcriptomics Steffen Möller University of Rostock Proteome Center."— Presentation transcript:

1 Integration of Heterogeneous Informations Sources for Proteomics and Transcriptomics Steffen Möller University of Rostock Proteome Center

2 Data Flow and Motivation List of genes products with changed expression level Description of variants of genes Sample A.a Sample A.1 Group A Sample Z.z Sample Z.1 Group Z... Sample Selection Preparation Analysis Measurements Question Interpretation

3 Data available online Grouping of samples in homogeneous groups Portioning and preparation of samples Data derived from a preparation –DNA/RNA sequencing –Affymetrix Microarrays –2DE Gels –(Tandem) mass spectrometry External bioinformatics databases Internal extensions to the above –Communication of ideas between researchers Lab-internal information Measurements Aids for Interpretation of Data

4 Organisation of Samples

5 Access of MS Spectra MASCOT peptide identification MS/MS fragment sequencing

6 Addition to external data sources Genes discussed among researchers

7 Figure 1: The figure shows the integration of protein expression as derived from the analysis of the gel with the RNA expression from a chip experiment, represented as a linearly scaled yellow bar. The spot volume is equivalently depicted as in red, the area in green and the peak intensity in blue. Overview on Identified Spots on Gel Integration of Protein expression levels –Spot Volume –Spot Area –Spot Peak intensity with RNA expression levels –from Affymetrix chips

8 Application of Agent Technology Automated retrieval and integration of presumed relevant in-house data Assistance in interpretation –Heuristics to extend/shrink list of genes presumed relevant Integration with external online data –Pathways –Known relevance of genes in other diseases

9 Data Flow Adapted for Agents: Input: List of Gene IDs Output: List of ( Gene ID Agent ID Evaluation Explanation History ) Seed of Genes Modified List of Genes Heuristic

10 Examples for Heuristics Towards extension/shrinking of list of genes under investigation –Gene lies within chromosomal locus linked to disease –Chromosomal neighbourhood to other genes of investigation –Gene is of presumed low abundance Guidance of further wet-lab analysis –Comparison of ration RNA/protein levels Search for pre- or post-transcriptional control

11 Example: Interaction with EnsEMBL Visualisation of QTLs with expression data (G. Fischer et al. 2002, submitted)

12 Transfer from Automated Sequence Annotation EDITtoTrEMBL (Möller et al. 1998) –Introduction of intermediate level for data integration –Hierarchical organisation of agents TrEMBL Program Integration

13 EDITtoTrEMBL: Self-introducing Agents Dispatchers provide automated planning of annotation path of entries Sequence-Analysing agents described their input and their output to dispatching agents SWISS-PROT syntax and controlled vocabulary Regular expressions as constraints

14 Application in sequence annotation of transmembrane proteins A variety of programs exist to predict –membrane spanning regions –direction of insertion into the membrane Out In

15 Conflict resolution Implemented with REVISE (C. V. Damasio; 1997) application described in (S. Möller, M. Schroeder; 2000)

16 Problems with the transfer of these techniques to the wet-lab Analysers cannot describe themselves or their results –No ontology for methods of expression data analysis has been defined (yet) –The motivation of an analyser to include a gene cannot be formally expressed No rules for conflict resolution applicable –Conflicts point the unexpected, not to artefacts

17 Discussion Should I implement the best possible agent system or rather ASAP hunt for the causing agents of autoimmune diseases? New agents are recruited from Perl scripts that are implemented to provide a quick answer to requests of biological researchers. Integration on a pragmatical level The system is accepted by wet-lab researchers. The system has a PHP-based web-frontend, –communication between agents is implemented via SOAP –adaptations and extensions to the system are easily implemented.

18 Acknowledgements University of Rostock Michael Kreutzer, Gertrud Fischer, Bernd Scheidt, Ines Weber, Angelika Allenberg, Björn Damm, Michael Glocker, Hans-Jürgen Thiesen City University, London Michael Schroeder EMBL-EBI, Cambridge Rolf Apweiler Funded by the BMBF Leitprojekt Proteom-Analyse des Menschen and the Landesforschungsschwerpunkt Genomorientierte Biotechnologie


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