Metagenomic mining of environmental resistomes: Comparative performance of deep learning and alignment-based annotation methods.

Authors

Joeselle M Serrana, Francisco J A Nascimento, Malte Posselt

Published in

iMetaOmics. Volume 3. Issue 2. Pages e70107. Epub Jun 09, 2026.

Abstract

Antimicrobial resistance (AMR) in marine environments poses intricate challenges due to the ecological complexity of microbial communities and environmental gradients. This study compares various antimicrobial resistance gene (ARG) annotation tools and databases, focusing on deep learning approaches versus traditional alignment-based methods, to evaluate their effectiveness in profiling the resistome of a complex marine ecosystem. Our report not only highlights the utility of deep learning methods for environmental applications but also proposes pathways for future improvements that can bridge the gap between comprehensive detection and clinically relevant risk interpretation.

PMID:
42891347
Bibliographic data and abstract were imported from PubMed on 18 Oct 2026.

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