Working group 4

Development of a bioinformatics infrastructure for specialized research in kidney and urine proteomics. This WG will focus on computational issues pertinent to renal and urine proteomics data representation, sharing, retrieval, quality assurance and control, integration and analysis. Specific tasks that will be carried out include:
  1. Identification of all available knowledge and data sources concerning kidney and urine proteomics and other -omics.  This will be followed by the development of an integrated system for querying heterogeneous information sources relevant to the topic (structured knowledge bases (e.g. UniProt), document collections (e.g. MedLine), image databases, etc.),  in view of retrieving and extracting specific information related to kidney and urine proteomics.
  2. Creation, maintenance and update of a specialized “urine and renal proteomics” database that will integrate existing and newly acquired proteomics data.
  3. Development of a specialized ontology on kidney and urine -omics and all related diseases.  The ontology will be anchored to top level concepts of reference ontologies such as Gene Ontology (for gene products) or FMA (for anatomy), as well as other available ontologies (myGrid, UTOPIA) and terminological resources used by kidney/urine specialists (e.g., MeSH, ICD) . The initial or core ontology on kidney and urine proteomics will be developed by domain specialists together with computer scientists experienced in the development and deployment of biomedical ontologies. The latter will also develop web-service based mechanisms for incremental and collaborative refinement of this ontology by domain experts.
  4. Design, implementation and optimization of a set of tools for pre-processing of, and knowledge discovery from, proteomics experimental data. Issues to be addressed include: the data high dimensionality-small sample size problem, the inherently noisy nature of the data, the stability and reproducibility of the produced models, the incorporation of domain knowledge into the knowledge discovery process using innovative statistical/bioinformatic approaches.
  5. Investigation of strategies and development of a set of procedures to control for the quality of the experimental data and learned models.
Chair: Erik Bongcam-Rudloff co-Chair: Teresa Attwood Group Member Users

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Victor Gerdin

LCB Uppsala University Sweden

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