MGS2AMR:  A Workflow to Mine Metagenomic Sequencing Data for Pathogens and their Antimicrobial Resistance Profile

Abstract:

The MGS2AMR pipeline detects antibiotic resistance genes (ARG) and their possible origin within metagenomic sequencing data. This in silico method mimics laboratory isolation, culturing, and sequencing of bacteria from a metagenome sample, allowing for the evaluation of bacterial antimicrobial resistance (AMR) directly from stool samples. The pipeline bypasses several hurdles of in vitro cultivation, such as different growth requirements for bacteria and the time-consuming process of the AMR testing. We have developed two new algorithms to optimize and annotate the genomic assembly paths within the raw Graphical Fragment Assembly (GFA): GFA Linear Optimal Path through Seed segments algorithm (GLOPS) and Adapted Dijkstra Algorithm for GFA (ADAG). These algorithms improve the sensitivity of ARG detection and aid in species annotation. The results of 1200 tests show a high ARG recall rate and correct assignment of the ARG origin. The MGS2AMR output can be used in many downstream applications, such as evaluation for AMR to specific AB in samples from emerging intestinal infection. This tool provides a researcher with valuable insights into AMR content of microbiome environments and may improve patient care by providing faster quantification of resistance against specific antibiotics and reducing the need for broad-spectrum antibiotics.

Reference:

P.-J. Van Camp, V. B. S. Prasath, D. B. Haslam, A. Porollo. MGS2AMR: A gene-centric mining of metagenomic sequencing data for pathogens and their antimicrobial resistance profile. Microbiome, 11, Article number 223, October 2023. doi:10.1186/s40168-023-01674-z (Github)

Under preparation:

Antimicrobial resistance evaluation of individual bacteria in metagenomic sequencing data using machine learning.