Predicting Antimicrobial Resistance Using Partial Genome Alignments

  17 June 2021

Antimicrobial resistance causes thousands of deaths annually worldwide. Understanding the regions of the genome that are involved in antimicrobial resistance is important for developing mitigation strategies and preventing transmission. Machine learning models are capable of predicting antimicrobial resistance phenotypes from bacterial genome sequence data by identifying resistance genes, mutations, and other correlated features. They are also capable of implicating regions of the genome that have not been previously characterized as being involved in resistance. In this study, we generated global chromosomal alignments for Klebsiella pneumoniae, Mycobacterium tuberculosis, and Salmonella enterica and systematically searched them for small conserved regions of the genome that enable the prediction of antimicrobial resistance phenotypes. In addition to known antimicrobial resistance genes, this analysis identified genes involved in virulence and transport functions, as well as many genes with no previous implication in antimicrobial resistance.

Further reading: mSystems
Author(s): Aytan-Aktug D, Nguyen M, Clausen PTLC, Stevens RL, Aarestrup FM, Lund O, Davis JJ.
Smart Innovations  


Unrestricted financial support by:

Antimicrobial Resistance Fighter Coalition


JSS University


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