Developing software to interpret microbial communities
The importance of the human associated microbiome is only being discovered now, with tremendous insights gained through metagenomics. In my lab we develop new methods to scale the usability of metagenomics in a clinical context. By describing the genetic potential of microbes, we can identify known pathogenicity and antibiotic resistance genes that these might carry in their genomes. This can ultimately help in targeting and adjusting the dosage of patient treatments, on a personalized basis.
Metagenomics is the description of microorganisms and their function through sequencing technology, gaining precision, high-throughput and standardisation while avoiding biases in culturing and microscopy based approaches.
In the Hildebrand lab we focus on the following themes
My group is investigating the immense diversity of bacteria and other microorganisms that can be found in microbiome the human gut (and other environments). This is because recent microbial studies have shown us, that we currently only see a glimpse of the immense genetic diversity of fungi, viruses and bacteria that each of us harbours. Some of these species are universally shared among humans, but often microbial species have yet to be catalogued and are only present in subsets of the population. Describing these new microbes with a patient centric view, is an approach of personalised microbiota care that we research.
For this we develop new algorithms that can extract metagenomically assembled genomes (MAGs) from metagenomes as well as high-resolution strain delineations.
The research lies in the intersection between microbial communities, bacterial strain evolution and genome plasticity, with the ultimate goal of understanding the microbial aetiology of a broad range of human diseases. Identifying bacterial strains that are associated to conditions such as IBD and Parkinson’s disease will help in exploring new treatments for these. In combination with genome reconstructions, we can compare genes that different strains within a human carry. Identifying novel taxa and placing them in a phylogenetic context helps in exploring the evolution of microbes that are associated to different environments.
To do this, we combine numerical ecology, population genetics and comparative genomics, together with laboratory methods that target specific fractions of any microbiome. One bioinformatic focus is on the accurate calling of genomic variants from highly complex metagenomics datasets and the delineation of patient specific bacteria.
We are looking for patterns in the composition of microbes in the human gut, to describe a) dysbiotic communities that can be associated to diseases like Type 2 Diabetes, IBD or Parkinson’s disease b) typical communities such as enterotypes and c) how microbial communities change with latitude in water and soil samples.
For these types of community analysis we rely on numerical ecology, machine learning and unsupervised clustering where we constantly adopt and develop our methods.
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