Characterization of microbial dark matter at scale with MetaSBT and taxonomy-aware Sequence Bloom Trees.

Accurately characterizing metagenome-assembled genomes remains a substantial challenge due to the presence of sequencing errors, incomplete assembly and contamination. Here, we present MetaSBT, a tool for organizing, indexing and characterizing microbial reference genomes and metagenome-assembled genomes, demonstrated in this study using viruses. MetaSBT identifies clusters of genomes across all seven taxonomic levels using the Sequence Bloom Tree data structure, which relies on Bloom filters to index large amounts of genomes based on their k-mer composition. We built an initial set of databases composed of over 190,000 viral genomes from public sources, grouped into sequence-consistent clusters at different taxonomic levels. We defined over 40,000 candidate species, ~80% of which, to our knowledge, do not match viral species in reference databases to date. Furthermore, we showed that our databases are useful to existing quantitative metagenomic profilers to unlock the detection of unknown microbes and the estimation of their abundance in metagenomic samples. The open-source framework and databases are fully integrated into the Galaxy platform.