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PathoFact 2.0: An Integrative Pipeline for Predicting Antimicrobial Resistance Genes, Virulence Factors, Toxins and Biosynthetic Gene Clusters in Metagenomes

Sunyer, J. O.; Delgado, L. F.; Hickl, O.; Laczny, C. C.; May, P.; Wilmes, P.

2024-12-11 bioinformatics
10.1101/2024.12.09.627531 bioRxiv
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SummaryAntimicrobial resistance genes (ARGs) and virulence factors (VFs) are central contributors to the global health crisis surrounding drug-resistant infections. PathoFact, a bioinformatics pipeline introduced in 2021, provides insights into ARGs, VFs, and bacterial toxins from metagenomic data. However, recent advancements in bioinformatics highlight the need for an updated version of PathoFact. We introduce PathoFact 2.0, an enhanced pipeline for improved ARG, VF, and toxin prediction. Key updates include an updated machine learning (ML) model for VF identification, a new ML model for toxin identification, expanded hidden Markov model profiles, and the antiSMASH 7.0 integration for predicting biosynthetic gene clusters. These upgrades make PathoFact 2.0 a more powerful, user-friendly platform for predicting microbiome-based pathogenicity and resistance, offering a crucial tool for better understanding and addressing the challenges posed by antimicrobial resistance and infectious diseases. Availability and ImplementationPathoFact 2.0 is available for download at https://gitlab.lcsb.uni.lu/ESB/PathoFact2/.

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The top 3 journals account for 50% of the predicted probability mass.

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"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.