ArticleBMC bioinformatics2025
A new alignment-free method: K-mer Subsequence Natural Vector (K-mer SNV) for classification of fungi.
Article in BMC bioinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Machine-learning-based analysis of host-depletedBioinformatics advances · 2026Article
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7 authors.
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Abstract
As eukaryotic organisms, fungi play a pivotal role within ecosystems and exert profound influences on agriculture, the pharmaceutical industry, and human health. The classification of fungi in databases has emerged as a crucial and complex issue in the field of biology. In this study, by leveraging the local distribution of k-mer in nucleotide sequences, we introduce a novel alignment-free method, denoted as k-mer SNV, to address this challenge. On a large fungi dataset including 120,140 sequences, our innovative approach has achieved remarkable success in predicting the taxonomic labels of fungi across six hierarchical taxonomic levels: phylum (99.52%), class (98.17%), order (97.20%), family (96.11%), genus (94.14%), and species (93.32%). The approach is also evaluated on the common Taxxi benchmark dataset. Based on these results, it has been convincingly demonstrated that the k-mer SNV method exhibits outstanding performance in processing large-scale fungal sequence data.
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