An interlaboratory proficiency test using metagenomic sequencing as a diagnostic tool for the detection of RNA viruses in swine fecal material.

Metagenomic shotgun sequencing (mNGS) can serve as a generic molecular diagnostic tool. An mNGS proficiency test (PT) was performed in six European veterinary and public health laboratories to detect porcine astroviruses in fecal material and the extracted RNA. While different mNGS workflows for the generation of mNGS data were used in the different laboratories, the bioinformatic analysis was standardized using a metagenomic read classifier as well as read mapping to selected astroviral reference genomes to assess the semiquantitative representation of astrovirus species mixtures. All participants successfully identified and classified most of the viral reads to the two dominant species. The normalized read counts obtained by aligning reads to astrovirus reference genomes by Bowtie2 were in line with Kraken read classification counts. Moreover, participants performed well in terms of repeatability when the fecal sample was tested in duplicate. However, the normalized read counts per detected astrovirus species differed substantially between participants, which was related to the different laboratory methods used for data generation. Further modeling of the mNGS data indicated the importance of selecting appropriate reference data for mNGS read classification. As virus- or sample-specific biases may apply, caution is needed when extrapolating this swine feces-based PT for the detection of other RNA viruses or using different sample types. The suitability of experimental design to a given pathogen/sample matrix combination, quality assurance, interpretation, and follow-up investigation remain critical factors for the diagnostic interpretation of mNGS results. IMPORTANCE: Metagenomic shotgun sequencing (mNGS) is a generic molecular diagnostic method, involving laboratory preparation of samples, sequencing, bioinformatic analysis of millions of short sequences, and interpretation of the results. In this paper, we investigated the performance of mNGS on the detection of porcine astroviruses, a model for RNA viruses in a pig fecal material, among six European veterinary and public health laboratories. We showed that different methods for data generation affect mNGS performance among participants and that the selection of reference genomes is crucial for read classification. Follow-up investigation remains a critical factor for the diagnostic interpretation of mNGS results. The paper contributes to potential improvements of mNGS as a diagnostic tool in clinical settings.