The project develops new techniques for the interactive navigtion, visualization, and analysis of heterogeneous biological networks
Zusammenfassung der Projektergebnisse
The objective of this project was the development of novel visual analytics techniques for biological data in the context of networks. The core components of the visual analytics framework are automated methods for data integration, efficient data analysis methods, and sophisticated graph drawing techniques. Within this project, we designed, implemented and tested a software framework for biological data integration, automated data analysis, and visual analysis of the resulting data. The complex integration of publicly available biological network data with high-throughput data (transcriptomics, proteomics, metabolomics) can be achieved conveniently with this framework, reducing the barrier of entrance for inexperienced users. Customized data visual analytics tools for clinical applications (e.g., personalized cancer therapy) have been built on top of this infrastructure. They combine efficient data analysis methods (e.g., identification of deregulated connected subnetworks) with novel visualization methods for these biological networks. We have applied these tools to a number of biological and medical applications and can demonstrate the scalability of the framework as well as the user benefit of the tools. All results of this project are available as opensource software to the scientific community and led to many scientific publications. In particular, we organized the project in five working packages WP1-WP5. The first four work packages formed the technological core for the customized visual analytics tools that have been developed and tested for biomedical applications in WP5. WP1 was concerned with issues related to data management, versioning, and update of the biological data from public databases. WP2 was concerned with the implementation of a data integration middleware layer enabling interactive response times for navigational queries. We developed the UniPAX framework enabling data integration of external network data with high-throughput data in a generic and abstract manner. WP3 dealt with the continuing development of our network visualization framework BiNA and the design of novel visualization and navigation techniques. BiNA serves as our visual analysis frontend for the users, and as interface between the other components and work packages. It provides visualization methods for various network types and powerful functionalities for visual analysis of enhanced network data. In the context of KEGG maps, we developed a customized approach for automatic layout and user support for organism specific queries. WP4 dealt with the combination of WP1-3 into a visual analytics framework based on fast databases, a unifying object model implemented in an efficient middleware, and the construction of customized applications for specific visual analytics tasks arising in the biomedical applications of WP5. In WP5 we developed tools for the detection of deregulated pathogenic processes. In close collaboration with clinical groups we have also applied our tools to elucidate pathogenic processes, for examples in lung tumors and Wilms tumors. Many groups worldwide have applied our tools, in particular GeneTrail, to analyze pathogenic processes in/for a variety of diseases.
Projektbezogene Publikationen (Auswahl)
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(2012). An integer linear programming approach for finding deregulated subgraphs in regulatory networks. Nucleic Acids Res 40, e43
Backes, C., Rurainski, A., Klau, G.W., Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., Lenhof, H.-P.
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The mzQuantML data standard for mass spectrometry-based quantitative studies in proteomics. Mol Cell Proteomics. 2013 Aug;12(8):2332-40
Walzer, M., Qi, D., Mayer, G., Uszkoreit, J., Eisenacher, M., Sachsenberg, T., Gonzalez-Galarza, F.F., Fan, J., Bessant, C., Deutsch, E.W., Reisinger, F., Vizcaíno, J.A., Medina-Aunon, J.A., Albar, J.P., Kohlbacher, O., Jones, A.R.
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(2014 A) BiNA: A Visual Analytics Tool for Biological Network Data. PLoS ONE 9(2): e87397
Gerasch, A., Faber, D., Küntzer, J., Niermann, P., Kohlbacher, O., Lenhof, H-P., Kaufmann, M.
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(2014) Rebuilding KEGG Maps: Algorithms and Benefits. PacificVis : 97-104
Gerasch A., Kaufmann, M., Kohlbacher, O.
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(2014). Myeloid cell RelA/p65 promotes lung cancer proliferation through Wnt/β-catenin signaling in murine and human tumor cells. Oncogene 33, 1239- 248
Li, D., Beisswenger, C., Herr, C., Hellberg, J., Han, G., Zakharkina, T., Voss, M., Wiewrodt, R., Bohle, R.M., Menger, M.D., Schmid, R.M., Stöckel, D., Lenhof, H.-P., Bals, R.
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The mzTab data exchange format: communicating mass-spectrometry-based proteomics and metabolomics experimental results to a wider audience. Mol Cell Proteomics. 2014 Oct;13(10):2765-75
Griss, J., Jones, A.R., Sachsenberg, T., Walzer, M., Gatto, L., Hartler, J., Thallinger, G.G., Salek, R.M., Steinbeck, C., Neuhauser, N., Cox, J., Neumann, S., Fan, J., Reisinger, F., Xu, Q.W., Del Toro, N., Pérez- Riverol, Y., Ghali, F., Bandeira, N., Xenarios, I., Kohlbacher, O., Vizcaíno, J.A., Hermjakob, H.
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Time-resolved characterization of cAMP/PKA-dependent signaling reveals that platelet inhibition is a concerted process involving multiple signaling pathways. Blood. 2014 Jan 30;123(5):e1-e10
Beck, F., Geiger, J., Gambaryan, S., Veit, J., Vaudel, M., Nollau, P., Kohlbacher, O., Martens, L., Walter, U., Sickmann, A., Zahedi, R.P.
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(2016). Multi-omics Enrichment Analysis using the GeneTrail2 Web Service. Bioinformatics, Volume 32, Issue 10, 15 May 2016, Pages 1502–1508
Stöckel, D., Kehl, T., Trampert, P., Schneider, L., Backes, C., Ludwig, N., Gerasch, A., Kaufmann, M., Gessler, M., Graf, N., Meese, E., Keller, A., and Lenhof, H.-P.
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DrugTargetInspector: An assistance tool for patient treatment stratification. International Journal of Cancer Vol 138 Issue 7, 1 April 2016, Pages 1765-1776
Schneider, L., Stöckel, D., Kehl, T., Gerasch, A., Ludwig, N., Leidinger, P., Huwer, H., Tenzer, S., Kohlbacher, O., Hildebrandt, A., Kaufmann, M., Gessler, M., Keller, A., Meese, E., Graf, N., Lenhof, H.-P.
