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Our Bioinformatics Research & Development, Publications

The mission of the Central Infrastructure Group Bioinformatics is to promote the research activities at the MPIMP by providing high-level bioinformatics service and data analysis support. By collaborating with other MPIMP research groups, and also in pursuit of its own line of research, the Bioinformatics group engages in a broad range of bioinformatics research and development activities. They center around the following three areas: OMICs data analysis and Systems Biology, sequence-structure-function relationships in RNA molecules, and database and tool development.

Our Publications

Tools & Services

Selected grant-funded joint projects: GoFORSYS, SEPSAPE

We collaborate closely with the AG Bioinformatics of the University Potsdam headed by Joachim Selbig.

Research and Development Projects

Figure 1: The glycolysis pathway and the physical interactions between participating enzymes denoted by black lines. (From Durek & Walther, 2008, BMC Syst Biol)

OMICS data analysis and Systems Biology

The interrogation of cellular systems by means of OMICS technologies that aim to capture an entire complement of a particular level of molecular organization has become common practice. Given the breadth of OMICS technologies covering transcriptomics, proteomics, and metabolomics applied and developed by our experimental partner groups at the institute, the group actively participates in the analysis of the generated large datasets. A particular focus is the study of dynamic phenomena from time series data to deduce causal relationships (Walther et al. (2010) OMICS J Integr Biol) and to describe other dynamic characteristics such as complexity (Sun et al. (2010) BMC Bioinformatics). By integrating datasets covering different levels of molecular organization, the group aims to reveal overarching principles governing the organization of molecular networks. For example, we studied the relationships between the physical interactions between enzymes and the underlying metabolic pathway relationships (Durek & Walther (2008) BMC Syst Biol, Figure 1).

Fig. 2
Figure 2: Sequence-structure relationships in RNA loops. Schudoma et al. 2009, Nucl Acids Res 38(3)

Understanding the sequence-structure-function relationships of RNA molecules

The realization that RNA molecules serve many more functions and evidence that much more genomic material is transcribed into RNA than previously thought belong to the seminal discoveries in molecular biology in recent years. The new interest in RNA is further fueled by the technological advances allowing to efficiently sequence hundreds of thousands to millions of RNA molecules in parallel (Next Generation Sequencing (NGS) technologies). Identifying functional RNA molecules from such NGS datasets and from bioinformatically scanning genomes has become a major task also for our group. Our particular approach is to better understand the specific sequence-structure-function relationships of functional RNA molecules distinguishing them from non-functional candidates (Childs et al. (2009) Nucl Acids Res) and to reveal the principles governing the folding of RNA with a particular focus on RNA-loops (Schudoma et al. (2009) Nucl Acids Res, Figure 2).

Figure 3: Screenshot of the GMD

Databases and software solutions

The investigation of metabolomic processes in plants is a central research theme pursued at the institute. Supporting these activities by developing databases and associated analysis tools is a focus of our work. In particular, together with Dr. Joachim Kopka and his group, we are developing the Golm Metabolome Database (GMD,, Figure 3). As part of the GMD and also in the context of different metabolomics technologies (GC/LC/FTIR-MS), we devise novel methods for the automated processing of metabolomics datasets (Hummel et al. (2010) Metabolomics). Data warehousing and providing integrated data views are main challenges of Systems Biology. As partners in a number of Systems Biology projects, we develop various database solutions and web-based visualization and query solutions (e.g. for the GoFORSYS Systems Biology project).

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