Work

PathoFact 2.0: an integrative pipeline for the prediction of antimicrobial resistance genes, virulence factors, toxins and toxin-associated proteins, and biosynthetic gene clusters in metagenomes

Year: 2026

Type: article

Abstract: BACKGROUND: Antimicrobial resistance genes (ARGs) and virulence factors (VFs) are central contributors to the global health crisis surrounding drug-resistant infections. FINDINGS: We introduce PathoFact 2.0, an enhanced pipeline for improved ARG, VF, toxin, and biosynthetic gene clusters (BGCs) prediction. Key improvements include an updated machine learning (ML) model for VF identification, expanded hidden Markov model profiles for VFs and toxin-associated proteins, a new ML model for toxin and toxin-associated proteins identification, and the integration of antiSMASH 7.0 for predicting BGCs. CONCLUSIONS: Our upgrades make PathoFact 2.0 a more powerful and user-friendly platform for predicting microbiome-based pathogenicity and resistance, providing a crucial tool for better understanding and addressing the challenges posed by antimicrobial resistance and infectious diseases.PathoFact 2.0 is available at https://gitlab.com/uniluxembourg/lcsb/systems-ecology/pathofact2. It is compatible with Linux operating systems.

Source: GigaScience

DOI: 10.1093/gigascience/giag062

Authors: Luis F Delgado, Júlia Ortís Sunyer, Cedric C Laczny, Oskar Hickl, Patrick May, Paul Wilmes

Institutions: University of Luxembourg, Luxembourg Centre for Systems Biomedicine

Language: English

Topic: Antibiotic Resistance in Bacteria

Subfield: Molecular Medicine

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,638