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dbcan-nf: a Nextflow pipeline for accurate CAZyme annotation in metagenomes
Xinpeng Zhang1, Haidong Yi2, Jinfang Zheng3
1Department of Food Science and Technology, Nebraska Food for Health Center, University of Nebraska-Lincoln, Lincoln, NE 68588, United States.
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.
