# DeepTaxa, a QIIME 2 plugin for 16S taxonomy

**URL:** <https://forum.qiime2.org/t/deeptaxa-a-qiime-2-plugin-for-16s-taxonomy/34228>\
**Category:** Community Contributions\
**Tags:** classifier, 16s, greengenes2, taxonomy\
**Created:** [June 25, 2026, 6:33am UTC](https://forum.qiime2.org/t/deeptaxa-a-qiime-2-plugin-for-16s-taxonomy/34228 "2026-06-25T06:33:57Z")\
**Posts on this page:** 2\
**Page:** 1

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**Author:** ![ahmedmoustafa](https://forum.qiime2.org/user_avatar/forum.qiime2.org/ahmedmoustafa/32/21839_2.png) [@ahmedmoustafa](https://forum.qiime2.org/u/ahmedmoustafa)\
**Post date:** [June 25, 2026, 6:33am UTC](https://forum.qiime2.org/t/deeptaxa-a-qiime-2-plugin-for-16s-taxonomy/34228/1 "2026-06-25T06:33:57Z")

</div>

Hi everyone,

I want to share a QIIME 2 plugin that we just released for DeepTaxa. Maybe it is useful for your 16S analysis.

DeepTaxa is a deep learning model for the taxonomy of 16S rRNA sequences. It predicts all seven ranks, from domain to species, in a single step using a hybrid CNN-BERT network. On the Greengenes2 2024.09 test set, it reaches about 93% accuracy at the species level. We describe the method in our paper in _Bioinformatics Advances_ ([https://doi.org/10.1093/bioadv/vbag166](https://doi.org/10.1093/bioadv/vbag166)).

The plugin is called `q2-deeptaxa`. It adds three actions:

- `qiime deeptaxa classify`: assign taxonomy to your sequences using a trained model
- `qiime deeptaxa fit`: train a model from your own reference sequences and taxonomy
- `qiime deeptaxa describe`: print a summary of a trained model

The trained model is a regular QIIME 2 artifact (the type is `DeepTaxaModel`), so QIIME tracks its provenance like any other artifact. The output of `classify` is a normal `FeatureData[Taxonomy]`, so you can use it with `taxa barplot` and the other QIIME tools.

To install (we tested this on Linux):

```auto
conda env create -n deeptaxa -f https://raw.githubusercontent.com/systems-genomics-lab/deeptaxa/main/environment-files/deeptaxa-qiime2-amplicon-2024.10.yml
conda activate deeptaxa
qiime dev refresh-cache
qiime deeptaxa --help

```

Then download a pretrained model for your amplicon region from our Hugging Face page ( [systems-genomics-lab/deeptaxa · Hugging Face](https://huggingface.co/systems-genomics-lab/deeptaxa) ), import it, and run classify:

```auto
qiime tools import \
  --type DeepTaxaModel \
  --input-path deeptaxa-full-length-v1.pt \
  --input-format DeepTaxaModelFormat \
  --output-path model.qza

qiime deeptaxa classify \
  --i-reads rep-seqs.qza \
  --i-classifier model.qza \
  --o-classification taxonomy.qza

```

The result is a taxonomy table. After you export it, one row looks like this:

```auto
Feature ID Taxon Confidence
seq1 d __Bacteria; p__ Pseudomonadota; c __Gammaproteobacteria; o__ Enterobacterales; f __Enterobacteriaceae; g__ Escherichia 0.97

```

By default, `classify` stops the lineage when the confidence drops below 0.7, the same idea as `classify-sklearn`. If you want all seven ranks, add `--p-confidence disable`. It works on CPU or GPU. A GPU is faster for big runs, but it is not required.

The code, a walkthrough, and tutorials are on GitHub ( [GitHub - systems-genomics-lab/deeptaxa: Hybrid CNN-BERT deep learning for hierarchical taxonomic classification of 16S rRNA sequences · GitHub](https://github.com/systems-genomics-lab/deeptaxa) ). We are happy to receive your feedback; please let us know if anything does not work.

Thank you,  
Ahmed

---

<div class="post-metadata">

**Author:** ![ahmedmoustafa](https://forum.qiime2.org/user_avatar/forum.qiime2.org/ahmedmoustafa/32/21839_2.png) [@ahmedmoustafa](https://forum.qiime2.org/u/ahmedmoustafa)\
**Post date:** [July 2, 2026, 10:46am UTC](https://forum.qiime2.org/t/deeptaxa-a-qiime-2-plugin-for-16s-taxonomy/34228/2 "2026-07-02T10:46:59Z")

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We have written a [step-by-step tutorial](https://systems-genomics-lab.github.io/deeptaxa/qiime2.html) that walks through the `q2-deeptaxa` workflow from start to finish. It imports a DeepTaxa model, classifies representative sequences into `FeatureData[Taxonomy]`, inspects the result, and also covers `describe` and `fit`. The worked example uses a real 16S V4 dataset (an ALS gut-microbiome case study), and every command shows its actual output, including a genus-level bar plot.

Tutorial: [https://systems-genomics-lab.github.io/deeptaxa/qiime2.html](https://systems-genomics-lab.github.io/deeptaxa/qiime2.html).