快速全基因组测序的出现为从基因组数据计算预测抗菌素耐药性(AMR)表型创造了新的机会。基于规则的方法和机器学习(ML)方法都已经被探索用于这项任务,但是仍然需要系统的基准测试。这里,我们评估了四种最先进的ML方法(Kover,PhenotypeSeeker,Seq2Geno2Pheno和Aytan-Aktug),ML基线和基于规则的ResFinder,通过在78个物种抗生素数据集中对它们进行培训和测试,使用严格的基准工作流程,集成了三种评估方法,每个配对三种不同的样品分割方法。我们的分析显示,技术和数据集之间的性能差异很大。而ML方法通常优于密切相关的菌株,ResFinder擅长处理不同的基因组。总的来说,Kover最常在ML方法中排名第一,其次是PhenotypeSeeker和Seq2Geno2Pheno。预测了抗生素类的AMR表型,例如大环内酯类和磺胺类。不同物种-抗生素组合的预测质量差异很大,特别是β-内酰胺类;跨物种,β-内酰胺类化合物的抗性表型,氨曲南,阿莫西林/克拉维酸,头孢西丁,头孢他啶和哌拉西林/他唑巴坦,与其他基准抗生素相比,四环素类药物表现出更多的可变性能。按有机体,空肠弯曲菌和屎肠球菌的表型比大肠杆菌的预测更为稳健,金黄色葡萄球菌,肠沙门氏菌,淋病奈瑟菌,肺炎克雷伯菌,铜绿假单胞菌,鲍曼不动杆菌,肺炎链球菌和结核分枝杆菌。此外,我们的研究为每个物种-抗生素组合提供了软件建议.它进一步强调了对稳健临床应用的优化需求,特别是对于与用于训练的菌株大不相同的菌株。
The advent of rapid whole-genome sequencing has created new opportunities for computational prediction of antimicrobial resistance (AMR) phenotypes from genomic data. Both rule-based and machine learning (ML) approaches have been explored for this task, but systematic benchmarking is still needed. Here, we evaluated four state-of-the-art ML methods (Kover, PhenotypeSeeker, Seq2Geno2Pheno and Aytan-Aktug), an ML baseline and the rule-based ResFinder by training and testing each of them across 78 species-antibiotic datasets, using a rigorous benchmarking workflow that integrates three evaluation approaches, each paired with three distinct sample splitting methods. Our analysis revealed considerable variation in the performance across techniques and datasets. Whereas ML methods generally excelled for closely related strains, ResFinder excelled for handling divergent genomes. Overall, Kover most frequently ranked top among the ML approaches, followed by PhenotypeSeeker and Seq2Geno2Pheno. AMR phenotypes for antibiotic classes such as macrolides and sulfonamides were predicted with the highest accuracies. The quality of predictions varied substantially across species-antibiotic combinations, particularly for beta-lactams; across species, resistance phenotyping of the beta-lactams compound, aztreonam, amoxicillin/clavulanic acid, cefoxitin, ceftazidime and piperacillin/tazobactam, alongside tetracyclines demonstrated more variable performance than the other benchmarked antibiotics. By organism, Campylobacter jejuni and Enterococcus faecium phenotypes were more robustly predicted than those of Escherichia coli, Staphylococcus aureus, Salmonella enterica, Neisseria gonorrhoeae, Klebsiella pneumoniae, Pseudomonas aeruginosa, Acinetobacter baumannii, Streptococcus pneumoniae and Mycobacterium tuberculosis. In addition, our study provides software recommendations for each species-antibiotic combination. It furthermore highlights the need for optimization for robust clinical applications, particularly for strains that diverge substantially from those used for training.