Enhance your clade prediction process with MLSTclassifier_cd, a powerful machine learning tool that employs K-Nearest Neighbors (KNN) algorithm. Designed specifically for Multi-Locus Sequence Type (MLST) analysis of C. difficile strains, including cryptic variants, this tool streamlines and accelerates clade prediction. MLSTclassifier_cd achieves a prediction accuracy of approximately 92%.
StatQuest methodology was used to build the model (https://www.youtube.com/watch?v=q90UDEgYqeI&t=3327s). Powered by the Scikit-learn library, MLSTclassifier_cd is a good tool to have a first classification of your C. difficile strains including cryptic ones.
The model was trained using data from PubMLST (May 2023): https://pubmlst.org/bigsdb?db=pubmlst_cdifficile_seqdef&page=downloadProfiles&scheme_id=1. Cryptic strains for training were assessed manually using phylogenetic tree construction, fastbaps and popPUNK to refine clustering.
GitHub repo: https://github.com/eliottBo/MLSTclassifier_cd
It is recommended to use a virtual environment.
Install PyPI package:
pip install mlstclassifier-cd
https://pypi.org/project/mlstclassifier-cd/
The query is a directory containing ".mlst" files like the ones retrieved on PubMLST or a path to a directory containing ".fastmlst" output files from FastMLST.
MLSTclassifier_cd [query directory path] [output file path]
Example: MLSTclassifier_cd /Desktop/directory_name /Desktop/directory_name/example_output.csv
After running MLSTclassifier_cd, the output file should contain an additional column named "predicted_clade". It also creates the following files:
- "pie_chart.html" displays the proportions of the different classes found.
- "count.csv" a csv file containing the raw value count of your predicted clades for you to generate your own graphs!