Publisher Information: Högskolan i Skövde, Institutionen för biovetenskap Högskolan i Skövde, Forskningsmiljön Systembiologi Department of Laboratory Medicine, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, Sweden 1928 Diagnostics, Gothenburg, Sweden Molecular Microbiology, Laboratory Medicine, Unilabs AB, Skövde, Sweden 2023
نبذة مختصرة : BACKGROUND: The rapidly growing area of sequencing technologies, and more specifically bacterial whole-genome sequencing, could offer applications in clinical microbiology, including species identification of bacteria, prediction of genetic antibiotic susceptibility and virulence genes simultaneously. To accomplish the aforementioned points, the commercial cloud-based platform, 1928 platform (1928 Diagnostics, Gothenburg, Sweden) was benchmarked against an in-house developed bioinformatic pipeline as well as to reference methods in the clinical laboratory. METHODS: Whole-genome sequencing data retrieved from 264 Staphylococcus aureus isolates using the Illumina HiSeq X next-generation sequencing technology was used. The S. aureus isolates were collected during a prospective observational study of community-onset severe sepsis and septic shock in adults at Skaraborg Hospital, in the western region of Sweden. The collected isolates were characterized according to accredited laboratory methods i.e., species identification by MALDI-TOF MS analysis and phenotypic antibiotic susceptibility testing (AST) by following the EUCAST guidelines. Concordance between laboratory methods and bioinformatic tools, as well as concordance between the bioinformatic tools was assessed by calculating the percent of agreement. RESULTS: There was an overall high agreement between predicted genotypic AST and phenotypic AST results, 98.0% (989/1006, 95% CI 97.3-99.0). Nevertheless, the 1928 platform delivered predicted genotypic AST results with lower very major error rates but somewhat higher major error rates compared to the in-house pipeline. There were differences in processing times i.e., minutes versus hours, where the 1928 platform delivered the results faster. Furthermore, the bioinformatic workflows showed overall 99.4% (1267/1275, 95% CI 98.7-99.7) agreement in genetic prediction of the virulence gene characteristics and overall 97.9% (231/236, 95% CI 95.0-99.2%) agreement in predict
CC BY 4.0The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/ zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.Correspondence: Anna‑Karin Pernestig anna‑karin.pernestig@his.seOpen access funding provided by University of Skövde. Swedish Knowledge Foundation BioMine Grant No. 206/0330, Swedish Knowledge Foundation Associate Senior Lecturer in Systems biology, Grant No. 2017/14, Stiftelsen Tornspiran, Internal research fund, Unilabs AB.The datasets generated and/or analysed during the current study are available in the online NCBI repository, https://www.ncbi.nlm.nih.gov/, BioProject PRJNA606666, http://www.ncbi.nlm.nih.gov/bioproject/606666
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