resistomedb

resistomedb profiles the abundance, distribution, and horizontal transfer potential of antibiotic resistance genes (ARGs) in TARA Oceans metagenomic samples to inform ecological and clinical analyses.

Key Features:

• Metagenomic analysis: Applies machine learning to detect and quantify ARGs in environmental metagenomic samples from the TARA Oceans project.
• ARG classification and abundance: Classifies open reading frames (ORFs), identifying 99,205 ORFs assigned to 560 different ARGs across 26 antibiotic classes.
• Plasmid-associated ARGs: Analyzes putative plasmid-like sequences and reports 24,567 ORFs within these sequences, highlighting mobile genetic element involvement.
• Multi-resistant contigs: Detects contigs containing multiple ARGs, including plasmid-like contigs harboring up to five different ARGs.
• Geographical and environmental correlations: Correlates ARG distribution with environmental and geographical parameters to assess spatial patterns.
• Clinical-relevant ARG detection: Identifies ARGs similar to mobilized colistin resistance genes (mcr), noting high abundance in polar biomes and presence in genera such as Psychrobacter.

Scientific Applications:

• Ecological surveillance: Characterizes natural reservoirs and global distribution of ARGs in ocean environments.
• Horizontal gene transfer studies: Evaluates plasmid-associated ARGs and multi-ARG contigs to investigate mobilome-mediated transmission.
• Biogeographical analysis: Identifies environmental and geographical hotspots of ARG prevalence and diversity.
• Clinical relevance assessment: Links environmental ARGs, including mcr-like genes and Psychrobacter-associated variants, to potential clinical concerns.

Methodology:

Machine learning detection and quantification of ARGs in TARA Oceans metagenomic samples, classification of ORFs, analysis of putative plasmid-like sequences and multi-ARG contigs, followed by manual curation of detected ARGs.