Fast ESKAPE bacterial genome classifier using k-mer profiles
Project description
BaClasT -- Bacterial Classification Tool
Fast classification of assembled bacterial genomes into ESKAPE pathogen species using k-mer frequency profiling.
Install
uv add baclast
CLI
baclast --predict genome.fna
baclast --predict genomes/ -o results.csv
Python
import src.classifier as baclast
baclast.predict(file="genome.fna")
baclast.to_csv(baclast.predict(file="genome.fna"), "results.csv")
What it classifies
ESKAPE pathogens (E. faecium, S. aureus, K. pneumoniae, A. baumannii, P. aeruginosa, E. cloacae) plus an "Other" class for non-ESKAPE bacteria. Includes centroid-based out-of-distribution detection.
How it works
Computes 4-mer frequency profiles (256 features) from genome assemblies and classifies with a Random Forest. A bundled pre-trained model is included -- no training data or setup required.
Requirements
Python >= 3.12, biopython, scikit-learn, joblib, numpy.
License
MIT
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file baclast-0.1.0.tar.gz.
File metadata
- Download URL: baclast-0.1.0.tar.gz
- Upload date:
- Size: 208.4 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: uv/0.7.16
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
10179a2ba7e6fe7a2e71287b500b28f7ef7d8d4e5f6e2574a529010c07693c6a
|
|
| MD5 |
e40e9cca59eb2b12924df49dcdd940f6
|
|
| BLAKE2b-256 |
3ca1a56f4ffc8c1e73320eb866b7188e9cb3ae9725cc71ce058d8a0d87cea61b
|
File details
Details for the file baclast-0.1.0-py3-none-any.whl.
File metadata
- Download URL: baclast-0.1.0-py3-none-any.whl
- Upload date:
- Size: 213.3 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: uv/0.7.16
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
137e0fa8a36e0f1a957ae72ef049f6697850fd1719f1877012146079f9e866d3
|
|
| MD5 |
22e0a57557af1782637129adf4cd7621
|
|
| BLAKE2b-256 |
2ae533b56065c6aa854a6afb47f5ada74bc0bdee5c4678180c9aaa2958b31a5d
|