DeepTaxa: A Hybrid CNN-BERT Framework for 16S rRNA Taxonomic Classification

Clicks: 1
ID: 317254
2026
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal

Ranked #82 of 104 articles by views in Bioinformatics advances

Most read Least read

Bar heights use a square-root scale.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
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 multi-scale 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 ≤0.0008 F1 at every rank), with F1 above 0.99 from domain through class and 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 Kraken2. 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. 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.
Reference Key
openalex_W7164669383 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Rana Salah, Khlood R. AbdElaal, Lobna Ghonaim, Olaitan I. Awe, Ahmed Moustafa
Journal Bioinformatics advances
Year 2026
DOI
10.1093/bioadv/vbag166
URL
https://doi.org/10.1093/bioadv/vbag166
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.