Background/Objectives: Rapid and accurate pathogen identification and antimicrobial susceptibility testing (AST) are critical for the proper management of patients with bloodstream infection (BSI). Real-time whole-genome sequencing (WGS) represents an attractive opportunity for exhaustive pathogen identification and antimicrobial susceptibility prediction (ASP). This feasibility study introduces BacT-Seq, a WGS-based prototype assay for the rapid and accurate identification of pathogens and the prediction of antimicrobial susceptibility from positive blood cultures using Oxford Nanopore Technologies (ONT) sequencing. Methods: A total of 200 positive blood culture samples from patients with a confirmed BSI were included in this study. DNA isolation from blood cultures was optimized prior to GridION (ONT) sequencing. Pathogen identification and several ASP methods were compared to conventional identification and phenotypic AST methods. Results: Most of the mono-microbial (89%) and poly-microbial (88%) samples were identified by BacT-Seq in less than 10 min of sequencing. While identification of poly-microbial samples remains challenging, identification of mono-microbial samples by sequencing was non-inferior to that of the conventional approach, even revealing an added value in terms of exhaustivity and/or taxonomic resolution. Machine-learning-based ASP models yielded 80% predictions in 2.5 h of sequencing. Their ability to predict resistance phenotypes varied with the microbial species evaluated, from 55/57 (96.5%) for Escherichia coli to 24/48 (50.0%) for Pseudomonas aeruginosa. Conclusions: This study demonstrates the feasibility of implementation of the BacT-Seq platform for the fast and accurate identification of pathogens from positive blood cultures. BacT-Seq performance of resistance predictions by bioinformatics tools is promising but requires further optimization and validation before clinical implementation.

Whole-genome sequencing has become an essential tool for real-time genomic surveillance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) worldwide. The handling of raw next-generation sequencing (NGS) data is a major challenge for sequencing laboratories. We developed an easy-to-use web-based application (EPISEQ SARS-CoV-2) to analyse SARS-CoV-2 NGS data generated on common sequencing platforms using a variety of commercially available reagents. This application performs in one click a quality check, a reference-based genome assembly, and the analysis of the generated consensus sequence as to coverage of the reference genome, mutation screening and variant identification according to the up-to-date Nextstrain clade and Pango lineage. In this study, we validated the EPISEQ SARS-CoV-2 pipeline against a reference pipeline and compared the performance of NGS data generated by different sequencing protocols using EPISEQ SARS-CoV-2. We showed a strong agreement in SARS-CoV-2 clade and lineage identification (>99%) and in spike mutation detection (>99%) between EPISEQ SARS-CoV-2 and the reference pipeline. The comparison of several sequencing approaches using EPISEQ SARS-CoV-2 revealed 100% concordance in clade and lineage classification. It also uncovered reagent-related sequencing issues with a potential impact on SARS-CoV-2 mutation reporting. Altogether, EPISEQ SARS-CoV-2 allows an easy, rapid and reliable analysis of raw NGS data to support the sequencing efforts of laboratories with limited bioinformatics capacity and those willing to accelerate genomic surveillance of SARS-CoV-2.