ArticleBioinformatics advances2026
DeepTaxa: a hybrid CNN-BERT framework for 16S rRNA taxonomic classification.
Article in Bioinformatics advances, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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5 authors.
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Abstract
Motivation: Accurate species-level classification of prokaryotic 16S rRNA sequences remains difficult: existing tools rely on exact alignment, k-mer heuristics, or phylogenetic placement and are limited by incomplete reference databases. Deep learning approaches in microbial genomics have focused largely on whole-genome metagenomics, leaving 16S taxonomy under-supported. Results: We present DeepTaxa, a hybrid CNN-BERT framework that pairs a multiscale CNN with a transformer trained from scratch on the DNABERT-2 BPE vocabulary, producing parallel rank-specific predictions across the seven Linnean ranks. On the Greengenes2 2024.09 test set, DeepTaxa achieves species-level accuracy of 92.96% and F1 of 0.9212 (3-seed mean; cross-seed standard deviation Availability and implementation: Source code, trained checkpoints for full-length 16S and V3-V4 amplicons, curated datasets, and reproducible workflows are publicly available at github.com/systems-genomics-lab/deeptaxa and huggingface.co/systems-genomics-lab/deeptaxa.
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