AUTHOR=Li Xuefei , Lin Jingxia , Hu Yongfei , Zhou Jiajian 

TITLE=PARMAP: A Pan-Genome-Based Computational Framework for Predicting Antimicrobial Resistance

JOURNAL=Frontiers in Microbiology

VOLUME=11

YEAR=2020

URL=https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2020.578795

DOI=10.3389/fmicb.2020.578795

ISSN=1664-302X

ABSTRACT=<p>Antimicrobial resistance (AMR) has emerged as one of the most urgent global threats to public health. Accurate detection of AMR phenotypes is critical for reducing the spread of AMR strains. Here, we developed PARMAP (Prediction of Antimicrobial Resistance by MAPping genetic alterations in pan-genome) to predict AMR phenotypes and to identify AMR-associated genetic alterations based on the pan-genome of bacteria by utilizing machine learning algorithms. When we applied PARMAP to 1,597 <italic>Neisseria gonorrhoeae</italic> strains, it successfully predicted their AMR phenotypes based on a pan-genome analysis. Furthermore, it identified 328 genetic alterations in 23 known AMR genes and discovered many new AMR-associated genetic alterations in ciprofloxacin-resistant <italic>N. gonorrhoeae</italic>, and it clearly indicated the genetic heterogeneity of AMR genes in different subtypes of resistant <italic>N. gonorrhoeae</italic>. Additionally, PARMAP performed well in predicting the AMR phenotypes of <italic>Mycobacterium tuberculosis</italic> and <italic>Escherichia coli</italic>, indicating the robustness of the PARMAP framework. In conclusion, PARMAP not only precisely predicts the AMR of a population of strains of a given species but also uses whole-genome sequencing data to prioritize candidate AMR-associated genetic alterations based on their likelihood of contributing to AMR. Thus, we believe that PARMAP will accelerate investigations into AMR mechanisms in other human pathogens.</p>