mlstclassifier_cd 0.2.0

multi-locus sequence type clade classifier for C.difficile

MLSTclassifier_cd

Table of Contents

• Overview

• Installation

• Usage

• Output

Overview

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 accuracy of approximately 92% for predictions.

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

Installation:

It is recommended to use a virtual environment.

Install PyPI package: pip install mlstclassifier-cd

https://pypi.org/project/mlstclassifier-cd/

Usage:

The query can be a .txt file (as the one extracted from PubMLST), a csv file with the same structure as the example "MLST_file_example.csv" or an output file from FastMLST.

Make sure you match the input file extension with the input type argument:

• Text file (.txt) from PubMLST: pubmlst argument;
• CSV file with the same structure as "MLST_file_example.csv": csv argument;
• Output from FastMLST: fastmlst argument.

Basic Command:

MLSTclassifier_cd [query file path] [output path] [input type]

Example: MLSTclassifier_cd /Desktop/folder_name/MLST_file_example.csv /Desktop/folder_name/exmple_output.csv csv

Output:

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!