Rhizosphere 16S rRNA gene amplicon data, PICRUSt2-predicted functional profiles, tuber GC-MS/MS and amino-acid data, plot-level yield and N/C analysis, postharvest storage data, and tuber mineral composition from a potato field trial with an endophytic bacterial consortium.

This research-level project framework provides methodological guidance for analyzing multi-omics datasets derived from agricultural field trials utilizing endophytic bacterial consortia. The study focuses on the computational processing and statistical evaluation of rhizosphere microbiome profiles, specifically targeting 16S rRNA gene amplicon sequences and PICRUSt2-predicted functional pathways. Through structured data analysis pipelines, the relationship between microbial community structures and crop performance metrics is systematically evaluated. The analytical scope encompasses the integration of tuber biochemical profiles, including gas chromatography-tandem mass spectrometry (GC-MS/MS) metabolomic data, quantitative amino acid concentrations, and mineral composition analyses. Additionally, the framework supports the correlation of these biological indicators with agronomic outcomes, such as plot-level crop yield, nitrogen-to-carbon (N/C) ratios,

and postharvest storage durability. By establishing robust bioinformatics and statistical workflows, this research direction facilitates a comprehensive understanding of plant-microbe interactions and their direct influence on crop quality and yield optimization. The proposed methodology serves as a structured guide for implementing multivariate statistical models and predictive functional profiling in agricultural biotechnology systems.

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