Keeping up with the pathogens: improved antimicrobial resistance detection and prediction from Pseudomonas aeruginosa genomes

Antimicrobial resistance (AMR) is a growing concern, particularly in Pseudomonas aeruginosa due to increasing multi- and pan-drug resistance rates. Shotgun sequencing is gaining traction for in silico AMR profiling, but accurate prediction from P. aeruginosa genomes remains an unsolved problem. A study curated the most comprehensive database of known P. aeruginosa AMR variants and compared it with three previously published tools. The ARDaP software and associated AMR variant database provide an accurate tool for predicting AMR phenotypes in P. aeruginosa, surpassing current tools. Implementing ARDaP for routine AMR prediction will improve AMR identification, but knowledge gaps remain in understanding the P. aeruginosa resistome, particularly the basis of colistin AMR.