Infectious diseases are caused by microorganisms, such as bacteria,

viruses, parasites, or fungi and result in millions of deaths per year

worldwide. The increasing availability of omics data from next

generation sequencing or mass spectrometry platforms allow a deep

insight into the genome, transcriptome, or proteome of a pathogen and

its interaction with the human host.

We develop algorithms and statistical procedure which facilitate the

analysis of these large-scale data sets and allow integration of results

across experiments. In particular, we are interested in the following

key questions: How can we analyze metaomics experiments and pinpoint the

constituents of complex environmental samples if possible at high

resolution to the species or strain-level in limited time? How can we

take advantage of proteogenomics and quantitative proteomics approaches

to decipher the coding information of the genome of non-model organisms

and to disentangle quantitative information for the systematic

understanding of infections? How can we interrelate experiments and

integrate epidemiological data to gain insight into outbreaks and

acquire causative relationships within infection chains.

While we develop novel methods, we are highly interested in bringing

them into application in close collaboration with experimental and

epidemiological groups inside the Robert Koch Institute as well as

outside. Applications are highly diverse and range from investigating

the spread of ebola to diagnosing cowpox virus and from molecular

understanding of chlamydia infection to deciphering the complex genome

of the highly pathogenic amoeba Balamuthia mandriallaris.