A machine learning algorithm for the automatic classification of genotypes into clonal lineages.

Premise: The prompt categorization of isolates into described clonal lineages is a key tool for the management of its associated disease, potato late blight. New isolates of this pathogen are currently classified by comparing their microsatellite genotypes with characterized clonal lineages, but an automated classification tool would greatly improve this process. Here, we developed a flexible machine learning-based classifier for genotypes.

Methods: The performance of different machine learning algorithms in classifying genotypes into its clonal lineages was preliminarily evaluated with decreasing amounts of training data. The four best algorithms were then evaluated using all collected genotypes.

Results: mlpML, cforest, nnet, and AdaBag performed best in the preliminary test, correctly classifying almost 100% of the genotypes. AdaBag performed significantly better than the others when tested using the complete data set (Tukey HSD  < 0.001). This algorithm was then implemented in a web application for the automated classification of genotypes, which is freely available at https://github.com/cpatarroyo/genotypeclas.

Discussion: We developed a gradient boosting-based tool to automatically classify genotypes into its clonal lineages. This could become a valuable resource for the prompt identification of clonal lineages spreading into new regions.