Sarand: Exploring Antimicrobial Resistance Gene Neighborhoods in Complex Metagenomic Assembly Graphs

Abstract

Antimicrobial resistance (AMR) is a major global challenge to human and animal health. The genomic element (e.g., chromosome, plasmid, and genomic islands) and neighbouring genes associated with an AMR gene play a major role in its function, regulation, evolution, and propensity to undergo lateral gene transfer. Therefore, characterising these genomic contexts is vital to effective AMR surveillance, risk assessment, and stewardship. Metagenomic sequencing is widely used to identify AMR genes in microbial communities, but analysis of short-read data offers fragmentary information that lacks this critical contextual information. Alternatively, metagenomic assembly, in which a complex assembly graph is generated and condensed into contigs, provides some contextual information but systematically fails to recover many mobile genetic elements. Here we introduce Sarand, a method that combines the sensitivity of read-based methods with the genomic context offered by assemblies by extracting AMR genes and their associated context directly from metagenomic assembly graphs. Sarand combines BLAST-based homology searches with coverage statistics to sensitively identify and visualise AMR gene contexts while minimising inference of chimeric contexts. Using both real and simulated metagenomic data, we show that Sarand outperforms metagenomic assembly and recently developed graph-based tools in terms of precision and sensitivity for this problem. Sarand (https://github.com/beiko-lab/sarand) enables effective extraction of metagenomic AMR gene contexts to better characterize AMR evolutionary dynamics within complex microbial communities.