Amrita Bharat

This study uses population genomics and machine learning to identify genetic markers predictive of antifungal resistance in Candida albicans, highlighting the need for expanded resistance catalogues.

This paper presents a workflow using long-read sequencing to rapidly identify microbial pathogens and antimicrobial resistance in bloodstream infections, achieving faster results than conventional methods.

The paper curates AMR mutations in fungi to create FungAMR, identifies universal resistance mutations, and provides a tool to screen genomes for AMR mutations.

This study traces the global emergence and evolutionary history of Candida auris using population genomic analyses of isolates from 19 countries, identifying clades, phylogeographic patterns, and antifungal resistance mechanisms.