DeepTaxa: a hybrid CNN-BERT framework for 16S rRNA taxonomic classification

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.

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 $\le 0.0008$ F1 at every rank), with F1 above 0.99 from domain through class and a species-level expected calibration error of 0.0242. DeepTaxa exceeds DADA2 (90.05%) and QIIME 2 (85.01%) at the species rank on the same held-out test set, with larger gains over the k-mer-based classifiers SINTAX and Kraken 2. Performance degrades smoothly with decreasing training-set similarity (species F1 from 0.95 to 0.45), and a dedicated V3-V4 amplicon checkpoint reaches 87.55% species accuracy from an approximately 420 bp window.

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.