Quality management and independent assessment of high-throughput sequencing-based virus diagnostics have not yet been established as a mandatory approach for ensuring comparable results. The sensitivity and specificity of viral high-throughput sequence data analysis are highly affected by bioinformatics processing using publicly available and custom tools and databases and thus differ widely between individuals and institutions. Here we present the results of the COMPARE [Collaborative Management Platform for Detection and Analyses of (Re-)emerging and Foodborne Outbreaks in Europe] in silico virus proficiency test. An artificial, simulated in silico data set of Illumina HiSeq sequences was provided to 13 different European institutes for bioinformatics analysis to identify viral pathogens in high-throughput sequence data. Comparison of the participants’ analyses shows that the use of different tools, programs, and databases for bioinformatics analyses can impact the correct identification of viral sequences from a simple data set. The identification of slightly mutated and highly divergent virus genomes has been shown to be most challenging. Furthermore, the interpretation of the results, together with a fictitious case report, by the participants showed that in addition to the bioinformatics analysis, the virological evaluation of the results can be important in clinical settings. External quality assessment and proficiency testing should become an important part of validating high-throughput sequencing-based virus diagnostics and could improve the harmonization, comparability, and reproducibility of results. There is a need for the establishment of international proficiency testing, like that established for conventional laboratory tests such as PCR, for bioinformatics pipelines and the interpretation of such results.

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Shiga toxin-producing Escherichia coli (STEC) strains can colonize cattle for several months and may, thus, serve as gene reservoirs for the genesis of highly virulent zoonotic enterohemorrhagic E. coli (EHEC). Attempts to reduce the human risk for acquiring EHEC infections should include strategies to control such STEC strains persisting in cattle. We therefore aimed to identify genetic patterns associated with the STEC colonization type in the bovine host. IMPORTANCERuminants, especially cattle, are sources of food-borne infections by Shiga toxin-producing Escherichia coli (STEC) in humans. Some STEC strains persist in cattle for longer periods of time, while others are detected only sporadically. Persisting strains can serve as gene reservoirs that supply E. coli with virulence factors, thereby generating new outbreak strains. Attempts to reduce the human risk for acquiring STEC infections should therefore include strategies to control such persisting STEC strains. By analyzing representative genes of their core and accessory genomes, we show that bovine STEC with a persistent colonization type emerged independently from sporadically colonizing isolates and evolved in parallel evolutionary branches. However, persistent colonizing strains share similar sets of accessory genes. Defining the genetic patterns that distinguish persistent from sporadically colonizing STEC isolates will facilitate the targeted design of new intervention strategies to counteract these zoonotic pathogens at the farm level.

Data sharing enables research communities to exchange findings and build upon the knowledge that arises from their discoveries. Areas of public and animal health as well as food safety would benefit from rapid data sharing when it comes to emergencies. However, ethical, regulatory, and institutional challenges, as well as lack of suitable platforms which provide an infrastructure for data sharing in structured formats often lead to data not being shared, or at most shared in form of supplementary materials in journal publications. Here, we describe an informatics platform that includes workflows for structured data storage, managing and pre-publication sharing of pathogen sequencing data and its analysis interpretations with relevant stakeholders.

Data sharing enables research communities to exchange findings and build upon the knowledge that arises from their discoveries. Areas of public and animal health as well as food safety would benefit from rapid data sharing when it comes to emergencies. However, ethical, regulatory and institutional challenges, as well as lack of suitable platforms which provide an infrastructure for data sharing in structured formats, often lead to data not being shared or at most shared in form of supplementary materials in journal publications. Here, we describe an informatics platform that includes workflows for structured data storage, managing and pre-publication sharing of pathogen sequencing data and its analysis interpretations with relevant stakeholders.

No abstract