phylotypy 0.2.7

Naive Bayes Classifier for 16S rRNA sequence data

phylotypy

A Naive Bayesian Classifier for 16S rRNA gene sequences, inspired by the phylotypr R package by Riffomonas. Designed for classifying amplicon sequence variants (ASVs) from DADA2, QIIME2, or raw FASTA files against a reference database of 16S rRNA sequences. The RDP training data is provided here in the data directory located at the github repository. But Silva and others can be used.

Thanks to Riffomonas for the inspiration — check out the videos on his YouTube channel.

---

Performance

Training on the full RDP reference database takes ~30 seconds on a 2020 Apple Intel MacBook Pro. Newer systems should see a substantial increase in performance.

---

How to Install

Using pip:

pip install phylotypy

Using uv (recommended — how to install uv):

uv pip install phylotypy

Note: Intel Mac (x86_64) users are limited to numba 0.62.1, which is pinned in this package. Apple Silicon (M-series) users are not affected.

---

Training Data

Download the RDP reference training set and an example dataset before classifying:

File	Description
rdp_16S_v19.dada2.fasta	RDP trainset19072023, DADA2 format
dna_moving_pictures.fasta	Example dataset (Moving Pictures study)
The training data fasta descriptions should a taxonomy. By default the Species level is ignored.

"Kingdom", "Phylum", "Class", "Order", "Family", "Genus", "Species"

The taxon string in the fasta description should follow the semicolon-separated format like this:

>Bacteria;Pseudomonadota;Gammaproteobacteria;Enterobacterales;Enterobacteriaceae;Citrobacter
TAGAGTTTGATCCATGGCTCAGATTGAACGCTGGCGGCAGGCCTAACAC.....

---

Quick Start

1. Load training data and sequences to classify

from phylotypy import classifier, results, read_fasta

rdp = read_fasta.read_taxa_fasta("rdp_16S_v19.dada2.fasta")
moving_pics = read_fasta.read_taxa_fasta("dna_moving_pictures.fasta")

2. Train the classifier

database = classifier.make_classifier(rdp)

3. Classify sequences

classified = classifier.classify_sequences(moving_pics, database)

4. Format and export results

classified = results.summarize_predictions(classified)
print(classified.columns)

Output:

Index(['id', 'sequence', 'classification', 'Kingdom', 'Phylum', 'Class',
       'Order', 'Family', 'Genus', 'observed', 'lineage'],
      dtype='object')

classified.to_csv("classified_results.csv")

---

Complete Code Block

from phylotypy import classifier, results, read_fasta

rdp = read_fasta.read_taxa_fasta("rdp_16S_v19.dada2.fasta")
moving_pics = read_fasta.read_taxa_fasta("dna_moving_pictures.fasta")

database = classifier.make_classifier(rdp)

classified = classifier.classify_sequences(moving_pics, database)
classified = results.summarize_predictions(classified)
print(classified.head())

classified.to_csv("classified_results.csv")

---

Example Classification Output

Taxonomic levels (Domain → Genus) are semicolon-separated. Numbers in parentheses represent bootstrap confidence scores. The default confidence threshold is 80%.

Bacteria(100);Pseudomonadota(99);Alphaproteobacteria(99);Rhodospirillales(99);Acetobacteraceae(99);Roseomonas(83)

Bacteria(99);Bacteroidota(97);Bacteroidia(93);Bacteroidales(93);Bacteroidales_unclassified(93);Bacteroidales_unclassified(93)

Bacteria(100);Bacteroidota(100);Bacteroidia(100);Bacteroidales(100);Bacteroidaceae(100);Bacteroides(100)

---

Working with Your Own Data

phylotypy works with FASTA files from DADA2, QIIME2, or any standard pipeline. See read_fasta.py for utilities to load and convert sequence data into the required format.

A complete walkthrough is available in vignette.py.

---

Requirements

Dependencies are installed automatically via pip. See pyproject.toml for the full list.

---

Citation

If you use phylotypy in your research, please cite:

• Wang, Q., Garrity, G.M., Tiedje, J.M., Cole, J.R. (2007) Naive Bayesian Classifier for Rapid Assignment of rRNA Sequences into the New Bacterial Taxonomy. Applied and Environmental Microbiology, 73(16), 5261–5267.
• Schloss PD.2025.phylotypr: an R package for classifying DNA sequences. Microbiol Resour Announc14:e01144-24.https://doi.org/10.1128/mra.01144-24
• Saltikov, C. (2024) phylotypy: Python implementation of a Naive Bayesian 16S rRNA classifier. https://github.com/csaltikov/phylotypy