Enhancing predictions of antimicrobial resistance of pathogens by expanding the potential resistance gene repertoire using a pan-genome-based feature selection approach

BMC Bioinformatics

Methodology

Yu-Wei Wu¹  Ming-Ren Yang² 

[1] Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, 250 Wuxing St., Sinyi District, 11031, Taipei, Taiwan;Clinical Big Data Research Center, Taipei Medical University Hospital, 11031, Taipei, Taiwan;Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, 250 Wuxing St., Sinyi District, 11031, Taipei, Taiwan;Department of Electrical Engineering, National Taiwan University of Science and Technology, 106, Taipei, Taiwan;

关键词: Antimicrobial resistance;    Pan-genome;    Feature selection;    eXtreme gradient boosting;    XGBoost;    Hypothetical proteins;   

DOI  :  10.1186/s12859-022-04666-2

 received in 2022-03-23, accepted in 2022-04-04,  发布年份 2022

来源: Springer

【 摘 要 】

BackgroundPredicting which pathogens might exhibit antimicrobial resistance (AMR) based on genomics data is one of the promising ways to swiftly and precisely identify AMR pathogens. Currently, the most widely used genomics approach is through identifying known AMR genes from genomic information in order to predict whether a pathogen might be resistant to certain antibiotic drugs. The list of known AMR genes, however, is still far from comprehensive and may result in inaccurate AMR pathogen predictions. We thus felt the need to expand the AMR gene set and proposed a pan-genome-based feature selection method to identify potential gene sets for AMR prediction purposes.ResultsBy building pan-genome datasets and extracting gene presence/absence patterns from four bacterial species, each with more than 2000 strains, we showed that machine learning models built from pan-genome data can be very promising for predicting AMR pathogens. The gene set selected by the eXtreme Gradient Boosting (XGBoost) feature selection approach further improved prediction outcomes, and an incremental approach selecting subsets of XGBoost-selected features brought the machine learning model performance to the next level. Investigating selected gene sets revealed that on average about 50% of genes had no known function and very few of them were known AMR genes, indicating the potential of the selected gene sets to expand resistance gene repertoires.ConclusionsWe demonstrated that a pan-genome-based feature selection approach is suitable for building machine learning models for predicting AMR pathogens. The extracted gene sets may provide future clues to expand our knowledge of known AMR genes and provide novel hypotheses for inferring bacterial AMR mechanisms.

【 授权许可】

CC BY   
© The Author(s) 2022

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