GROOT

GROOT profiles antimicrobial resistance genes in metagenomic datasets using variation graph representations, a locality-sensitive hashing Forest index, and hierarchical local alignment against graph traversals to enable accurate resistome classification and reconstruction.

Key Features:

• Variation Graph Representation: Utilizes a variation graph representation of gene sets to accommodate high similarity among reference genes and genetic variation during read classification.
• Locality-Sensitive Hashing Forest Indexing: Incorporates a locality-sensitive hashing Forest indexing scheme to enable fast similarity-search queries for sequence-read classification.
• Hierarchical Local Alignment: Performs hierarchical local alignment of reads against graph traversals with a scoring scheme to reconstruct full-length gene sequences accurately.
• Performance Efficiency: Processes typical 2-gigabyte metagenomes in approximately 2 minutes on a single CPU, demonstrating high speed and accuracy.
• Versatility Beyond Resistome Profiling: Employs a methodology applicable to broader metagenomic workflows beyond antimicrobial resistance gene analysis.

Scientific Applications:

• AMR Surveillance: Enables comprehensive surveillance of antimicrobial resistance genes in environmental and clinical metagenomic samples.
• Distribution and Evolution Analysis: Supports analysis of the distribution and evolution of AMR genes across samples and environments.
• Transmission Dynamics: Facilitates investigation of transmission dynamics of resistance genes between hosts and environments.
• Informing Interventions and Treatment: Provides rapid and precise identification of AMR genes to inform targeted interventions and personalized treatment strategies.

Methodology:

Represents gene sets as variation graphs, indexes graph traversals with a locality-sensitive hashing Forest for similarity-search classification, and applies hierarchical local alignment with a scoring scheme to reconstruct full-length gene sequences.