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  Projects lead by the DIG group

DIG

The proteome databases and knowledge integration (DIG) axis is one of the three main research axes of PIG. Its objective is to help the analysis and interpretation of proteomics data motivated by the resolution of biological problems. Its main goals are data management and storage; data modelling and structuring; integration of experimental data and annotations and interpretation of integrated data.

Data management and storage

Proteomics databases are a first step towards analysing and rationalising proteomics data. As part of a collaboration with the wet-lab teams of Prof. Denis Hochstrasser and Dr. Jean-Charles Sanchez at the Biomedical Proteomics Research Group of Geneva University, the PIG group has created in 1993 a database of annotated protein maps, the SWISS-2DPAGE database. This database contains nearly 40 reference maps (images of 1-DE or 2-DE gels) from various species including human, mouse, Escherichia coli, etc. In addition to maintaining, annotating and developing the database, the group has created the software Make2D-DB to help naïve users to easily build their own 2-DE gel databases on their own Websites. Make2D-DB provides various keyword search mechanisms and the ability to perform queries through a graphical interface. The SWISS-2DPAGE database and databases created with the help of Make2D-DB are all interconnected among them and with the Swiss-Prot knowledgebase.

The SWISS-2DPAGE database, together with the WORLD-2DPAGE List of 2-D PAGE database servers, the World-2DPAGE Portal that queries simultaneously world-wide proteomics databases, and the World-2DPAGE Repository, compose today the World-2DPAGE Constellation of the ExPASy server.

To complete the range of databases available on the ExPASy server, the PIG group and Nicole Packer (Macquarie University, Sydney, Australia) are working together since 2008 to provide free access to the GlycoSuiteDB. The database content will evolve through collaborative work with glycobiologists.

In the context of the study about grid-based analysis of high-throughput MS/MS data, the group has reanalysed the GeneProt/MicroProt2 dataset using an in-house developed platform called swissPIT. The GeneProt/MicroProt2 database contains the result of this analysis and can be queried on the ExPASy server.

Tools and databases developed by the group:


Publications


Metabolic networks

In collaboration with Bastien Chopard (Scientific and Parallel Computing, SIB, Geneva)

Biological networks are expected to help understanding processes taking place in cells. Different biological networks can be represented as graphs like protein-protein interactions, gene regulatory networks, cell signaling networks, etc. Metabolic networks have been one of the first of those to be examined in order to understand and maybe model the metabolism of organisms. Different metabolic networks are already represented as graphs in databases like KEGG pathway and others. The way of separation into pathways in these databases is done out of historical and thematic reasons but in graph theory, different clustering algorithms have been developed which break up networks into modules which describes groups of vertices with many connections between them but little with vertices outside the group. This analysis is of crucial importance for network structure analysis.

The goal of this project is to compare mathematical clustering of a metabolic network from a database carried out by a computer to the original pathway assignment of a metabolic database. This analysis may reveal connections between reactions and metabolites not considered before.

Bioinformatics resources for the study of active peptides

In collaboration with Reto Stoecklin (Atheris, Geneva, Switzerland)

The project involves adapting and specialising customary MS analysis tools for the study of venoms. It also entails structuring heterogeneous data in the prospect of finding peptides with potential drug properties.

Classification methods for protein biomarker panels

In collaboration with Jean-Charles Sanchez ((Biomedical Proteomics Research Group, Medical and University Centre, Geneva, Switzerland)

Proteomics has enabled the discovery of numerous potential biomarkers for diagnosis and prognosis of various diseases. In order to be clinically useful, the prediction has to remain efficient with a sensitivity or specificity above 90%, so that at least one result (positive or negative) can be trusted. To date, however, only few biomarkers have proved to be efficient enough as a single analyte to be translated in clinical practice. A combination of biomarkers (panel) could help to solve this problem by improving the prediction power compared to single predictors. Our goal is to study different classification methods to improve the prediction of patients with a high specificity.

Publications


People working at DIG:

Last modified 21/Jan/2010 by PAP