dbcan-nf: a Nextflow pipeline for accurate CAZyme annotation in metagenomes.
- 1Department of Food Science and Technology, Nebraska Food for Health Center, University of Nebraska-Lincoln, Lincoln, NE 68588, United States.
- 2Center for Applied Bioinformatics, St. Jude Children's Research Hospital, Memphis, TN 38105, United States.
- 3Chongqing Institute of Intelligent Medicine (CIIM), Chongqing, 400042, China.
Abstract
Motivation
Carbohydrate-active enzymes (CAZymes) are crucial for carbohydrate degradation in any ecological environment. However, CAZyme annotation is not easy in whole-genome shotgun (WGS) metagenomes, due to the lack of accurate, scalable, robust, and reproducible workflows. We previously published dbCAN as a web server and associated standalone tool run_dbcan that performs CAZyme annotation with assembled genomes as the input but not with raw WGS reads.Results
Here, we present dbcan-nf, an accurate and scalable Nextflow pipeline integrating run_dbcan as a component step, for automated CAZyme annotation in metagenomes. dbcan-nf supports multiple analytical routes, including individual sample assembly (IA), multisample coassembly (CA), assembly-free (AF), and subsampling (SS), and accepts both short and long reads. Using five benchmark datasets, we showed that dbcan-nf (IA and AF routes) had a much higher accuracy (recall and F1 score) and detected more CAZyme families than the competing tool Cayman. Among the different routes, dbcan-nf-IA produced the most accurate CAZyme annotation, especially when the sequencing depth is sufficient (e.g., >1.0 Gb). However, as a read assembly-based method, dbcan-nf-IA had a higher computational cost than Cayman. Finally, dbcan-nf leverages Nextflow-supported high-performance computing execution for workflow-level scalability through concurrent processing of multiple samples.Availability and implementation
https://github.com/bcb-unl/dbcan-nf.
Related
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BibTeX
@article{zhang2026dbcannf,
title = {{dbcan-nf: a Nextflow pipeline for accurate CAZyme annotation in metagenomes.}},
author = {Xinpeng Zhang and Haidong Yi and Jinfang Zheng and Yanbin Yin},
journal = {Bioinformatics advances},
year = {2026},
month = oct,
doi = {10.1093/bioadv/vbag304},
url = {https://europepmc.org/article/MED/42899262},
note = {CC BY},
}
RIS
TY - JOUR
TI - dbcan-nf: a Nextflow pipeline for accurate CAZyme annotation in metagenomes.
AU - Xinpeng Zhang
AU - Haidong Yi
AU - Jinfang Zheng
AU - Yanbin Yin
PY - 2026
DA - 2026/10/07
JO - Bioinformatics advances
DO - 10.1093/bioadv/vbag304
UR - https://europepmc.org/article/MED/42899262
AB - Motivation Carbohydrate-active enzymes (CAZymes) are crucial for carbohydrate degradation in any ecological environment. However, CAZyme annotation is not easy in whole-genome shotgun (WGS) metagenomes, due to the lack of accurate, scalable, robust, and reproducible workflows. We previously published dbCAN as a web server and associated standalone tool run_dbcan that performs CAZyme annotation with assembled genomes as the input but not with raw WGS reads.Results Here, we present dbcan-nf, an accurate and scalable Nextflow pipeline integrating run_dbcan as a component step, for automated CAZyme annotation in metagenomes. dbcan-nf supports multiple analytical routes, including individual sample assembly (IA), multisample coassembly (CA), assembly-free (AF), and subsampling (SS), and accepts both short and long reads. Using five benchmark datasets, we showed that dbcan-nf (IA and AF routes) had a much higher accuracy (recall and F1 score) and detected more CAZyme families than the competing tool Cayman. Among the different routes, dbcan-nf-IA produced the most accurate CAZyme annotation, especially when the sequencing depth is sufficient (e.g., >1.0 Gb). However, as a read assembly-based method, dbcan-nf-IA had a higher computational cost than Cayman. Finally, dbcan-nf leverages Nextflow-supported high-performance computing execution for workflow-level scalability through concurrent processing of multiple samples.Availability and implementation https://github.com/bcb-unl/dbcan-nf.
SN - 2635-0041
ER -