Comparing antimicrobial resistance prediction pipelines from bacterial whole genome sequencing data: An inter-laboratory study

Antimicrobial resistance (AMR) poses one of the greatest modern threats to public health. Clinical microbiology laboratories typically rely on culturing bacteria for antimicrobial susceptibility testing (AST). As the implementation costs and technical barriers fall, whole-genome sequencing (WGS) has emerged as a ‘one-stop’ test for epidemiological and predictive AST results. Few published comparisons exist for the myriad analytical pipelines used for predicting AMR. To address this, we performed an inter-laboratory study providing participants with identical short-read WGS data sequenced from clinical isolates, allowing us to assess the reproducibility of the bioinformatic prediction of AMR between laboratories and identify problem cases and factors that lead to discordant results. We produced ten sequencing datasets of varying quality from cultured carbapenem-resistant organisms obtained from clinical samples sequenced on either an Illumina NextSeq or HiSeq instrument. Nine laboratories were provided with this sequence data alone without any other contextual information. Each laboratory used their own pipeline to determine the species, the presence of resistance-associated genes, and to predict susceptibility or resistance to amikacin, gentamicin, ciprofloxacin and cefotaxime.

Additional Information

Sequence read files for all samples used in this study have been deposited in the European Nucleotide Archive under the project accession number PRJEB34513 (2019).

Keywords

Antimicrobial resistance, Amikacin, Gentamicin, Ciprofloxacin, Cefotaxime