In the recent past, the interest in microbiome research has increased because studies have suggested a strong link of microbial communities to their hosts. Along with a beneficial role of microbes, bacteria and viruses can have harmful effects and this pathogenic potential is of utmost interest. For public health, a robust and fast detection of pathogens is critical because it reduces the time for diagnosis and treatment and can lower the risk of transmission and mortality. This requires diagnostic assays to cover a broad spectrum for various potentially disease-causing agents.
In the past, genome sequencing technologies have demonstrated both the immense variety of pathogens and the complexity of microbial communities. Reaching beyond the genetic potential that is addressed by metagenomics, metaproteomics enables investigating the metabolic and cellular pathways in which microbial enzymes are key players. Proteomics has reached a mature stage where it cannot only be used in a research context, but also for diagnostics as it had been demonstrated with newly emerging SARS-CoV-2 applications.
This thesis aims at investigating the potential of using meta-omics technology and corresponding bioinformatics methods to process, analyze, and interpret data from experimental pathogen and microbiome samples. This work focuses on developing computational methods for detecting and characterizing bacterial and viral pathogens as well as for integrating host and microbiome data at different meta-omics levels. In addition, both experimental and bioinformatics benchmarking studies are performed. Finally, the aim is also to integrate methods and data for multiple omics levels, including metagenomics, metatranscriptomics, and metaproteomics.
