Next-generation metagenomic sequencing of rhizosphere soil from Polygonatum cyrtonema

To systematically characterize the functional profiles of non-redundant genes, sequence alignment and functional annotation were carried out against seven functional databases, namely KEGG, CAZy, CARD, VFDB, PHI, GO, and MetaCyc, using the DIAMOND alignment tool. The corresponding annotation rates for each database were 52.14%, 2.56%, 0.19%, 17.8%, 28.43%, 37.11%, and 34.14%, respectively, covering multiple functional dimensions including global metabolic pathways, carbohydrate-active enzymes, antibiotic resistance genes, virulence factors, pathogen-host interactions, gene ontology categories, and metabolic pathways.This dataset does not contain specific information on sample collection time, geographic coordinates, or corresponding spatiotemporal resolution.

All 24 samples have completed the full analytical workflow of quality control, assembly, gene prediction, and functional annotation, with intact core statistical data and no missing values. Inherent systematic errors from high-throughput sequencing and methodological errors introduced by sequence assembly and alignment exist during data generation and analysis. In this study, error levels were controlled through uniform quality control thresholds, a standardized analytical pipeline, and consistent alignment parameters.

The magnitude of errors falls within the generally acceptable range for metagenomic research, and the analytical results are stable and reproducible. All data files are stored in common bioinformatics formats such as FASTA and tab-delimited text, which can be directly read and analyzed using mainstream bioinformatics tools including BioPython and conventional text processing software, with no need for specialized niche software.