Proficiency testing of virus diagnostics based on bioinformatics analysis of simulated in silico high-throughput sequencing datasets
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. 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 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 dataset of Illumina HiSeq sequences was provided to 13 different European institutes for bioinformatics analysis towards the identification of 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 dataset. The identification of slightly mutated and highly divergent virus genomes has been identified as being 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 harmonization, comparability, and reproducibility of results. Similar to what is established for conventional laboratory tests like PCR, there is a need for the establishment of international proficiency testing for bioinformatics pipelines and interpretation of such results.