Modern high-throughput genetics, genomics, and proteomics approaches as performed at CNCR are associated with the production of vast amounts of experimental data. I aim to assist in data and bioinformatics analysis.
Modern high-throughput genetics, genomics, and proteomics approaches are associated with the production of vast amounts of experimental data. As a consequence, this type of research requires the use of information technology to store and efficiently retrieve experimental data for downstream analysis. Another challenge associated with large quantities of experimental data is the design and application of approaches for data analysis that are effective in answering biologically relevant questions.
The development and application of effective algorithms for data analysis in molecular biology is addressed by the ‘Bioinformatics’ and ‘Systems Biology’ disciplines, both of which are very active fields of science. At the CNCR the Applied Bioinformatics team is involved in the management and execution of all the stages of experimental data management and analysis as mentioned above. That includes the design and implementation of relational databases that allow for storage and efficient retrieval of both raw experimental data and of processed information derived of downstream analysis. For analysis of experimental data this team develops bioinformatics approaches that combine the application of established bioinformatics and systems biology software/approaches with the use of software tools and procedures that are produced in-house.
An important aim of the ‘Applied Bioinformatics’ team is to enable biologists of the CNCR to easily navigate and analyze experimental data. For this, web-based applications are designed that guide and assist users with the configuration of data analysis procedures and provide intuitive and flexible data mining capabilities on experimental results. On a higher level, the ‘Applied Bioinformatics’ team aims to provide a data analysis framework that allows for correlation analyses of experimental information from a multitude of data sources (both internal and external data repositories) and from a multitude of disciplines (genetics, genomics, proteomics).
An ongoing project of the team is the SynGO project that aims at annotating synaptic proteins framing these in a Synapse Ontology structure. The project is described here.