PT  - JOURNAL ARTICLE
AU  - Danielle E. Madden
AU  - Jessica R. Webb
AU  - Eike J. Steinig
AU  - Bart J. Currie
AU  - Erin P. Price
AU  - Derek S. Sarovich
TI  - Taking the next-gen step: comprehensive antimicrobial resistance detection from <em>Burkholderia pseudomallei</em> genomes
AID  - 10.1101/720607
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 720607
4099  - http://biorxiv.org/content/early/2020/08/26/720607.short
4100  - http://biorxiv.org/content/early/2020/08/26/720607.full
AB  - Background Antimicrobial resistance (AMR) poses a major threat to human health. Whole-genome sequencing holds great potential for AMR identification; however, there remain major gaps in comprehensively detecting AMR across the spectrum of AMR-conferring determinants and pathogens.Methods Using 16 wild-type Burkholderia pseudomallei and 25 with acquired AMR, we first assessed the performance of existing AMR software (ARIBA and CARD) for detecting clinically relevant AMR in this pathogen. B. pseudomallei was chosen due to limited treatment options, high fatality rate, and AMR caused exclusively by chromosomal mutation (i.e. single-nucleotide polymorphisms [SNPs], insertions-deletions [indels], copy-number variations [CNVs], and functional gene loss). Due to poor performance with existing tools, we developed ARDaP (Antimicrobial Resistance Detection and Prediction) to identify the spectrum of AMR-conferring determinants in B. pseudomallei.Results CARD failed to identify any clinically-relevant AMR in B. pseudomallei, ARIBA cannot differentiate AMR determinants from natural genetic variation, and neither CARD or ARIBA can identify CNV or gene loss determinants. In contrast, ARDaP accurately detected all SNP, indel, CNV, and gene loss AMR determinants described in B. pseudomallei (n≈50). Additionally, ARDaP accurately predicted three previously undescribed determinants. In mixed strain data, ARDaP identified AMR to as low as ~5% allelic frequency.Conclusions We demonstrate that existing AMR software are inadequate for comprehensive AMR detection; ARDaP overcomes the shortcomings of existing tools. Further, ARDaP enables AMR prediction from mixed sequence data down to 5% allelic frequency. ARDaP databases can be constructed for any microbial species of interest for comprehensive AMR detection.Competing Interest StatementThe authors have declared no competing interest.