Bioinformatics-Based Data Mining of GenBank and Diversity Patterns of Soil Fungal Sequences

Abstract

Soil fungi are key drivers of terrestrial ecosystem functioning, contributing to organic matter decomposition, nutrient cycling, and plant–microorganism interactions. Despite their importance, the global distribution and structural biases of public sequence records for soil fungi remain incompletely characterized. In this study, we analyzed soil-associated fungal DNA sequences retrieved from the NCBI GenBank database using a custom R-based bioinformatics pipeline. Following filtering and metadata standardization, 544,554 filtered sequence records were obtained. The taxonomic composition of the dataset consisted primarily of Ascomycota (69.62%), followed by Basidiomycota, Glomeromycota, and Mucoromycota, with Trichoderma, Penicillium, and Aspergillus representing the most frequent genera. The geographic distribution revealed strong sampling bias, with China and the United States accounting for over one-third of all records. Ecological metadata indicated that rhizospheric and forest soils were the most common sources of the deposited sequences. At the same time, gene marker analyses confirmed the widespread use of the ITS region as the primary fungal barcode. Sequence diversity analyses revealed continental variation, with Europe and Asia showing higher medians, while the ordination highlighted clustering of sequence profiles, particularly among records from extreme environments. This study demonstrates the potential of public sequence databases for large-scale biodiversity assessments while highlighting the influence of sampling bias and the limitations of metadata.