Confirmed speakers

Understanding community composition of different microbiomes using genome resolved metagenomics

The field of metagenomics has expanded rapidly over the past decade, both in terms of the number and diversity of different datasets. Furthermore, with application of either deep sequenced short-read or long-read sequencing the depth of the biological insight that can be gleaned from the sequence data has changed dramatically. One area of significant growth has been the assembly of datasets, and the subsequent elucidation of genomes, so called metagenome assembled genomes (MAG). In this presentation I will describe out latest efforts in understanding the microbial biodiversity found in different environmental samples. Having these large contiguous units also allows deeper insights into the metabolic functions encoded, and which microbes are producing them. For example, biosynthetic gene clusters (BGCs) encode the genes necessary for natural products such as antimicrobials and signalling molecules that can play major roles in various ecological processes. Many of these natural products have been exploited for industrial biotechnology or pharmaceutical applications. Thus, accurate identification of BGCs in (meta)genomic data is key to unveiling ecological dynamics and/or the discovery of new commercially important products. Thus, I will also present a new machine learning based tool for BGC-detection in either genomic or metagenomic assemblies, called SanntiS. Compared to other tools, our benchmarks show that our tool outperforms in the ability to detect BGCs across different classes, and notably retains precision in metagenomic datasets. Application to our metagenomic assemblies has revealed millions of potential BGCs, many of which are likely to give rise to new natural products.