A multicenter study on accuracy and reproducibility of nanopore sequencing-based genotyping of bacterial pathogens.

Nanopore sequencing has shown the potential to democratize genomic pathogen surveillance due to its ease of use and low entry cost. However, recent genotyping studies showed discrepant results compared to gold-standard short-read sequencing. Furthermore, although essential for widespread application, the reproducibility of nanopore-only genotyping remains largely unresolved. In our multicenter performance study involving five laboratories, four public health-relevant bacterial species were sequenced with the latest R10.4.1 flow cells and V14 chemistry. Core genome MLST analysis of over 500 data sets revealed highly strain-specific typing errors in all species in each laboratory. Investigation of the methylation-related errors revealed consistent DNA motifs at error-prone sites across participants at read level. Depending on the frequency of incorrect target reads, this either leads to correct or incorrect typing, whereby only minimal frequency deviations can randomly determine the final result. PCR preamplification, recent basecalling model updates and an optimized polishing strategy notably diminished the non-reproducible typing. Our study highlights the potential for new errors to appear with each newly sequenced strain and lays the foundation for computational approaches to reduce such typing errors. In conclusion, our multicenter study shows the necessity for a new validation concept for nanopore sequencing-based, standardized bacterial typing, where single nucleotide accuracy is critical.

Diversification of Pseudomonas aeruginosa Biofilm Populations under Repeated Phage Exposures Decreases the Efficacy of the Treatment.

Phage therapy has been proposed as a therapeutic alternative to antibiotics for the treatment of chronic, biofilm-related P. aeruginosa infections. To gain a deeper insight into the complex biofilm-phage interactions, we investigated in the present study the effect of three successive exposures to lytic phages of biofilms formed by the reference strains PAO1 and PA14 as well as of two sequential clinical P. aeruginosa isolates from the sputum of a patient with cystic fibrosis (CF). The Calgary device was employed as a biofilm model and the efficacy of phage treatment was evaluated by measurements of the biomass stained with crystal violet (CV) and of the cell density of the biofilm bacterial population (CFU/mL) after each of the three phage exposures. The genetic alterations of P. aeruginosa isolates from biofilms exposed to phages were investigated by whole-genome sequencing. We show here that the anti-biofilm efficacy of the phage treatment decreased rapidly with repeated applications of lytic phages on P. aeruginosa strains with different genetic backgrounds. Although we observed the maintenance of a small subpopulation of sensitive cells after repeated phage treatments, a fast recruitment of mechanisms involved in the persistence of biofilms to the phage attack occurred, mainly by mutations causing alterations of the phage receptors. However, mutations causing phage-tolerant phenotypes such as alginate-hyperproducing mutants were also observed. In conclusion, a decreased anti-biofilm effect occurred after repeated exposure to lytic phages of P. aeruginosa biofilms due to the recruitment of different resistance and tolerance mechanisms.

Managing and monitoring a pandemic: showcasing a practical approach for the genomic surveillance of SARS-CoV-2.

With the rapidly growing amount of biological data, powerful but also flexible data management and visualization systems are of increasingly crucial importance. The COVID-19 pandemic has more than highlighted this need and the challenges scientists are facing. Here, we provide an example and a step-by-step template for non-IT personnel to easily implement an intuitive, interactive data management solution to manage and visualize the high influx of biological samples and associated metadata in a laboratory setting. Our approach is illustrated with the genomic surveillance for SARS-CoV-2 in Germany, covering over 11 600 internal and 130 000 external samples from multiple datasets. We compare three data management options used in laboratories: (i) simple, yet error-prone and inefficient spreadsheets, (ii) complex and long-to-implement laboratory information management systems and (iii) high-performance database management systems. We highlight the advantages and pitfalls of each option and outline why a document-oriented NoSQL option via MongoDB Atlas can be a suitable solution for many labs. Our example can be treated as a template and easily adapted to allow scientists to focus on their core work and not on complex data administration.

Corrigendum: poreCov - An Easy to Use, Fast, and Robust Workflow for SARS CoV-2 Genome Reconstruction via Nanopore Sequencing.

[This corrects the article DOI: 10.3389/fgene.2021.711437.].

poreCov-An Easy to Use, Fast, and Robust Workflow for SARS-CoV-2 Genome Reconstruction via Nanopore Sequencing.

In response to the SARS-CoV-2 pandemic, a highly increased sequencing effort has been established worldwide to track and trace ongoing viral evolution. Technologies, such as nanopore sequencing via the ARTIC protocol are used to reliably generate genomes from raw sequencing data as a crucial base for molecular surveillance. However, for many labs that perform SARS-CoV-2 sequencing, bioinformatics is still a major bottleneck, especially if hundreds of samples need to be processed in a recurring fashion. Pipelines developed for short-read data cannot be applied to nanopore data. Therefore, specific long-read tools and parameter settings need to be orchestrated to enable accurate genotyping and robust reference-based genome reconstruction of SARS-CoV-2 genomes from nanopore data. Here we present poreCov, a highly parallel workflow written in Nextflow, using containers to wrap all the tools necessary for a routine SARS-CoV-2 sequencing lab into one program. The ease of installation, combined with concise summary reports that clearly highlight all relevant information, enables rapid and reliable analysis of hundreds of SARS-CoV-2 raw sequence data sets or genomes. poreCov is freely available on GitHub under the GNUv3 license: github.com/replikation/poreCov.