Human-Microbe Systems Bioinformatics
Jun Prof Dr Alexey Gurevich
The human body encompasses fewer human cells than microbes. They constantly interact with each other and the host and greatly affect an individual's health and well-being. In our group, we develop and apply state-of-the-art bioinformatics software to study the human-microbe systems and aim to discover natural products involved in communication between the two realms.
Our research and approach
Natural products are a promising source of new pharmaceuticals, such as anti-infectives. They also mediate human-microbe and microbe-microbe interactions and could serve clinicians as biomarkers of health and disease. Breakthroughs in omics technologies enabled the rapid acquisition of voluminous data on natural products and their producers, but the computational analysis and interpretation of these data remain a bottleneck.
We aim to turn natural product discovery into a high-throughput technology by developing and applying specialized computational tools. The close interaction with computer scientists and natural product experts makes the created software robust, efficient, and genuinely suitable for the local and international research community. The access to unique single- and multi-omics datasets available within collaborative projects represents a gold mine for tools’ validation and fine-tuning and ultimately leads to biomedically important discoveries and enhanced understanding of the complex human-microbe systems.
Integration of data from several omics technologies has great potential in natural product research. Recently, we utilized paired genomics and metabolomics datasets for the high-throughput discovery of ribosomal (RiPPs) and nonribosomal (NRPs) peptides. We work on extending this effort to other classes of natural products. We also consider adding more omics levels (transcriptomics, proteomics, metagenomics) to make the tools more specific and sensitive.
Biosynthetic gene clusters (BGCs) are responsible for the production of natural products and thus play a vital role in shaping microbial ecosystems and host-microbe interactions.
We predict BGCs from various microbiomes with genome mining software, correlate BGC abundance with metadata, such as the health state of the associated hosts, and identify the most clinically-important BGCs. To simplify further experimental validation, we develop computational methods for linking BGCs to their tentative final products.
Metagenomic sequencing enables the studying of complex microbiomes. However, metagenomic data interpretation requires multi-step computational pipelines involving dozens of tools, such as short/long read assemblers, binners, and taxonomic classifiers. We participate in the Critical Assessment of Metagenome Interpretation (CAMI) initiative that provides researchers and software developers with guidelines and best practices in metagenomic data analysis. In particular, we develop and maintain metaQUAST and QUAST, the leading software for (meta)genome assembly evaluation.