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AMRLearn: Protocol for a machine learning pipeline for characterization of antimicrobial resistance determinants in
Xi Zhang1, Yining Hu2, Zhenyu Cheng3
1Department of Biochemistry and Molecular Biology, Dalhousie University, Halifax, NS B3H 4R2, Canada; Institute for Comparative Genomics, Dalhousie University, Halifax, NS B3H 4R2, Canada.
None:
Single-nucleotide polymorphisms (SNPs) are useful biomarkers for linking genotype to phenotype. Machine learning is powerful for predicting antimicrobial resistance (AMR) from bacterial genome sequence data. Here, we present AMRLearn, a machine learning pipeline to assist users in the prediction and visualization of AMR phenotypes associated with SNP genotypes. We describe the steps needed for input data preparation, prediction model selection, and result visualization. AMRLearn is useful for researchers wanting to extract information relevant to AMR from whole-genome sequence data.
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