IntroductionThe objective of this study was to develop, using a genome wide machine learning approach, an unambiguous model to predict the presence of highly pathogenic STEC inE. colireads assemblies derived from complex samples containing potentially multipleE. colistrains. Our approach has taken into account the high genomic plasticity ofE. coliand utilized the stratification of STEC andE. colipathogroups classification based on the serotype and virulence factors to identify specific combinations of biomarkers for improved characterization ofeae-positive STEC (also named EHEC for enterohemorrhagicE.coli) which are associated with bloody diarrhea and hemolytic uremic syndrome (HUS) in human.MethodsThe Machine Learning (ML) approach was used in this study on a large curated dataset composed of 1,493E. coligenome sequences and 1,178 Coding Sequences (CDS). Feature selection has been performed using eight classification algorithms, resulting in a reduction of the number of CDS to six. From this reduced dataset, the eight ML models were trained with hyper-parameter tuning and cross-validation steps.Results and discussionIt is remarkable that only using these six genes, EHEC can be clearly identified fromE. coliread assemblies obtained from in silico mixtures and complex samples such as milk metagenomes. These various combinations of discriminative biomarkers can be implemented as novel marker genes for the unambiguous EHEC characterization from differentE. colistrains mixtures as well as from raw milk metagenomes.