InheritanceAlgorithm
Inheritance algorithm is a command line program written in Python that allows to attribute to each clonal group (CG), an identifier that would maximally reflect the widely adopted 7-gene ST identifier of the corresponding isolates.
Development a set of naming rules that prioritize the most abundant ST observed among isolates of each CG, as well as some supplementary rules in case of ties. This algorithm is summarized below, whereas an example is given in the technical notes pdf file.
Installation and execution
Clone this repository with the following command line:
git clone https://gitlab.pasteur.fr/BEBP/inheritance-algorithm.git
Verify that Python (3.6 or higher) is installed, as well as Pandas (x or higher) and NetworkX
Execute the file InheritanceAlgorithm.py
available inside the src directory with the following command line model:
python InheritanceAlgorithm.py [options]
Usage
Launch InheritanceAlgorithm with option -h
to read the following documentation:
usage: InheritanceAlgorithm [-h] -i FILEINPUT -c COLUMN_CG -o FILEOUTPUT
This tool to attribute to each clonal group (CG), an identifier that would maximally reflect the widely adopted 7-gene ST identifier of the corresponding isolates.
optional arguments:
-h, --help show this help message and exit
-i FILEINPUT (mandatory) input tab-delimited file containing the CGs associated for each isolate
-c COLUMN_CG (mandatory) column(s) of the selected CG(s)
-o FILEOUTPUT (mandatory) output file name
Example
The input file clustering.strain.txt inside the directory example contains three columns; the first one corresponds to the strain identifiers, the next two correspond to the associated CGs.
python InheritanceAlgorithm.py -i clustering.strain.txt -c Seuil43,Seuil190 -o result.txt
References
A dual barcoding approach to bacterial strain nomenclature: Genomic taxonomy of Klebsiella pneumoniae strains Melanie Hennart, Julien Guglielmini, Martin C.J. Maiden, Keith A. Jolley, Alexis Criscuolo, Sylvain Brisse bioRxiv 2021.07.26.453808; doi: https://doi.org/10.1101/2021.07.26.453808