Bioinformatics support infection research by means of enormous computing capacity and customized software© DZIF/science RELATIONS

Major technological advances during the last decade have resulted in massively decreased prices for DNA sequencing, while quality and information density have rapidly increased. These advancements will directly impact a multitude of research areas: investigation of host-pathogen interaction, genomic epidemiology, transcriptomics and regulatory networks, metagenomics, and the identification of epidemiologically distinct clones and their potential to develop into outbreak strains with extended pathogenic capabilities – all of those areas will benefit from these improvements.

The new generation of high-throughput methods has already surpassed the capacity of current software and IT structures, necessitating the application of effective bioinformatics tools. Scientific interpretation of data will only be possible by efficient preprocessing and analysis of data. Systematic collation technologies will be required to deal not only with the huge quantity of data generated but also to deal with the heterogeneity of the formats involved. The challenge is to generate aggregated information that can be used for further scientific interpretation.

This project will establish and provide tools to supply access to new technologies for respective research areas. In parallel to data management, statistical analysis methodologies will be implemented to deal with multi-dimensional data. The integration and comparative analysis of data generated within the DZIF Network is the basis for both interdisciplinary and translational research.

Bridging the gap between established genome bioinformatics and the medical data from clinical research will represent the main challenge for the DZIF unit “Bioinformatics Platform”. To this end, data from classical and genomic epidemiological investigations, from multifactorial multiple cases studies with typical medical parameters, and from preclinical and clinical trials will need to be correlated with genomic data.


Alice McHardy, Helmholtz Centre for Infection Research

Project manager
Andreas Bremges

Cristina della Beffa