Graph-based variant discovery reveals novel dynamics in the human microbiome

TL;DR

This study uses assembly graph analysis to detect complex structural variants in nearly 1,000 human microbiome metagenomes, revealing new insights into bacterial adaptation and phage discovery.

Contribution

It demonstrates a novel graph-based approach to identify structural variants in microbiome data, surpassing traditional SNP-focused methods.

Findings

Identified over nine million structural variants including insertions, deletions, and mobile elements.

Revealed variation rates differ across body sites, indicating niche-specific bacterial adaptation.

Discovered potential novel prophage integration events in microbial genomes.

Abstract

Sequence differences between the strains of bacteria comprising host-associated and environmental microbiota may play a role in community assembly and influence the resilience of microbial communities to disturbances. Tools for characterizing strain-level variation within microbial communities, however, are limited in scope, focusing on just single nucleotide polymorphisms, or relying on reference-based analyses that miss complex functional and structural variants. Here, we demonstrate the power of assembly graph analysis to detect and characterize structural variants in almost 1,000 metagenomes generated as part of the Human Microbiome Project. We identify over nine million variants comprising insertion/deletion events, repeat copy-number changes, and mobile elements such as plasmids. We highlight some of the potential functional roles of these genomic changes. Our analysis revealed striking differences in the rate of variation across body sites, highlighting niche-specific mechanisms of bacterial adaptation. The structural variants we detect also include potentially novel prophage integration events, highlighting the potential use of graph-based analyses for phage discovery.