Sequence-based modelling of bacterial genomes enables accurate antibiotic resistance prediction

Abstract Rapid detection of antibiotic-resistant bacteria and understanding the mecha- nisms underlying antimicrobial resistance (AMR) are major unsolved problems that pose significant threats to global public health. However, existing methods for predicting antibiotic resistance from genomic sequence data have had lim- ited success due to their inability to model epistatic effects and generalize to novel variants. Here, we present GeneBac, a deep learning method for predicting antibiotic resistance from DNA sequence through the integration of interactions between genes. We apply GeneBac to two distinct bacterial species and show that it can successfully predict the minimum inhibitory concentration (MIC) of multiple antibiotics. We use the WHO Mycobacterium tuberculosis mutation cat- alogue to demonstrate that GeneBac accurately predicts the effects of different variants, including novel variants that have not been observed during training. GeneBac is a modular framework which can be applied to a number of tasks including gene expression prediction, resistant gene identification and strain clus- tering. We leverage this modularity to transfer learn from the transcriptomic data to improve performance on the MIC prediction task.