A Novel Bioinformatics Pipeline and a Machine-Learning Approach for Antimicrobial Resistance Phenotypic Prediction

Overuse of antimicrobial drugs is known to cause an increase in bacterial resistance among surviving pathogens, reducing the effectiveness of future treatments. Publicly available sequencing collections, such as the National Center for Biotechnology Information Sequence Read Archive (SRA), allow for global investigation of antimicrobial resistance across pathogens. In this study, we developed a pipeline to process 10 803 globally sourced unique bacterial isolates from publicly available SRA datasets (9 pathogens, 4 antibiotics), representing sequencing data generated by multiple laboratories using diverse sequencing platforms and protocols. The pipeline extracted SRA metadata to determine read layout and length, applied quality control, trimming, and decontamination. Preprocessed isolates were mapped reads to an antimicrobial resistance gene class and to a strain-level genome reference library constructed from complete genomes on the SRA submitted between 2009 and 2020. Three classifiers—L1-penalized logistic regression, random forest, and extreme gradient boosting—were trained on the resulting feature matrices, and their outputs were combined in a majority-vote ensemble. Internal training resulted in 83.8% balanced accuracy on average, and external testing yielded 80.2%. Variable importance analyses identified known resistance gene classes and strain markers such as Acinetobacter baumannii LAC-4 and Campylobacter jejuni 81-176 and NCTC13255, confirming biological relevance. This work demonstrates a scalable approach for antimicrobial resistance prediction using heterogeneous sequencing data.