Annotation of Glycoside Hydrolases in Unassembled Metagenomes Using CAZyOGH

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ID: 320484
2026
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Abstract
Abstract Motivation Functional characterization of microbiomes often relies on the sequencing of metagenomic DNA extracted from environmental samples, with current approaches using metagenome-assembled genomes (MAGs). Although glycoside hydrolases (GHs) are central to carbon cycling, accurate annotation of GHs in metagenomic datasets remains challenging due to the multidomain architecture of carbohydrate-active enzymes and the prevalence of unassembled short reads due to limitations in the MAG-generation process. Results Here, we present CAZyOGH (CAZymes Open-source GH annotation), a curated reference database for the domain-specific identification of 135 protein domains spanning 99 GH families with well-defined catalytic domain signatures. CAZyOGH focuses on individual GH domains, enabling robust annotation of both assembled and unassembled metagenomic data. We validated CAZyOGH by reanalyzing genomes listed in CAZy db, where predicted GH profiles closely matched reported values. Next, we used CAZyOGH to analyze twelve human gut metagenomes and twelve newly sequenced soil microbiomes to reveal environment-specific GH repertoires. By accurately detecting catalytic domains independent of the genomic context, CAZyOGH improves sensitivity and specificity in short-read metagenomic annotation. This framework provides a scalable and reproducible approach to investigate carbohydrate-active enzymes across ecosystems, advancing our capacity to characterize microbial functional potential in global carbon cycling. Availability and implementation CAZyOGH data is available on figshare (https://figshare.com/projects/CAZyO_GH/267770).
Reference Key
openalex_W7167891643 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Nicholas G Griffin, Alison E. Hughes, Daniel S Erdody, Eliott Berlemont, Shannon Sweeney, Tara Fareghbal, K. B. Hagedorn, Renaud Berlemont
Journal Bioinformatics advances
Year 2026
DOI
10.1093/bioadv/vbag137
URL
https://doi.org/10.1093/bioadv/vbag137
Keywords Keywords not found

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