Indexed variation graphs for efficient and accurate resistome profiling

BackgroundAntimicrobial resistance remains a major threat to global health. Profiling the collective antimicrobial resistance genes within a metagenome (the \"resistome\") facilitates greater understanding of antimicrobial resistance gene diversity and dynamics. In turn, this can allow for gene surveillance, individualised treatment of bacterial infections and more sustainable use of antimicrobials. However, resistome profiling can be complicated by high similarity between reference genes, as well as the sheer volume of sequencing data and the complexity of analysis workflows. We have developed an efficient and accurate method for resistome profiling that addresses these complications and improves upon currently available tools.\n\nResultsOur method combines a variation graph representation of gene sets with an LSH Forest indexing scheme to allow for fast classification of metagenomic sequence reads using similarity-search queries. Subsequent hierarchical local alignment of classified reads against graph traversals enables accurate reconstruction of full-length gene sequences using a scoring scheme. We provide our implementation, GROOT, and show it to be both faster and more accurate than a current reference-dependent tool for resistome profiling. GROOT runs on a laptop and can process a typical 2 gigabyte metagenome in 2 minutes using a single CPU.\n\nConclusionWe present a method for resistome profiling that utilises a novel index and search strategy to accurately type resistance genes in metagenomic samples. The use of variation graphs yields several advantages over other methods using linear reference sequences. Our method is not restricted to resistome profiling and has the potential to improve current metagenomic workflows. The implementation is written in Go and is available at https://github.com/will-rowe/groot (MIT license).