The widespread use of High-Throughput Sequencing (HTS) for detection of plant viruses and sequencing of plant virus genomes has led to the generation of large amounts of data and of bioinformatics challenges to process them. Many bioinformatics pipelines for virus detection are available, making the choice of a suitable one difficult. A robust benchmarking is needed for the unbiased comparison of the pipelines, but there is currently a lack of reference datasets that could be used for this purpose. We present 7 semi-artificial datasets composed of real RNA-seq datasets from virus-infected plants spiked with artificial virus reads. Each dataset addresses challenges that could prevent virus detection. We also present 3 real datasets showing a challenging virus composition as well as 8 completely artificial datasets to test haplotype reconstruction software. With these datasets that address several diagnostic challenges, we hope to encourage virologists, diagnosticians and bioinformaticians to evaluate and benchmark their pipeline(s).

Application of high-throughput sequencing (HTS) technologies enabled the first identification of Physostegia chlorotic mottle virus (PhCMoV) in 2018 in Austria. Subsequently, PhCMoV was detected in Germany and Serbia on tomatoes showing severe fruit mottling and ripening anomalies. We report here how pre-publication data-sharing resulted in an international collaboration across eight laboratories in five countries enabling an in-depth characterization of PhCMoV. The independent studies converged toward its recent identification in eight additional European countries and confirmed its presence in samples collected 20 ago (2002). The natural plant host range was expanded from two species to nine species across seven families, and we confirmed the association of PhCMoV presence with severe fruit symptoms on economically important crops such as tomato, eggplant, and cucumber. Mechanical inoculations of selected isolates in greenhouse established the causality of the symptoms on a new indexing host range. In addition, phylogenetic analysis showed a low genomic variation across the 29 near-complete genomes sequences available. Furthermore, a strong selection pressure within a specific ecosystem was suggested by nearly identical sequences recovered from different host plants through time. Overall, this study describes the European distribution of PhCMoV on multiple plant hosts, including economically important crops which the virus can cause severe fruit symptoms for. This work demonstrates how to efficiently improve knowledge on an emergent pathogen by sharing HTS data, and provides a solid knowledge foundation for further studies on plant rhabdoviruses.

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Little cherry disease, caused by little cherry virus 1 (LChV-1) and little cherry virus 2 (LChV-2), which are both members of the family Closteroviridae, severely affects sweet (Prunus avium L.) and sour cherry (P. cerasus L.) orchards lifelong production worldwide. An intensive survey was conducted across different geographic regions of Belgium to study the disease presence on these perennial woody plants and related species. Symptomatic as well as non-symptomatic Prunus spp. trees tested positive via RT-PCR for LChV-1 and -2 in single or mixed infections, with a slightly higher incidence for LChV-1. Both viruses were widespread and highly prevalent in nearly all Prunus production areas as well as in private gardens and urban lane trees. The genetic diversity of Belgian LChV-1 and -2 isolates was assessed by Sanger sequencing of partial genomic regions. A total RNA High-Throughput Sequencing (HTS) approach confirmed the presence of both viruses, and revealed the occurrence of other Prunus-associated viruses, namely cherry virus A (CVA), prune dwarf virus (PDV) and prunus virus F (PrVF). The phylogenetic inference from full-length genomes revealed well-defined evolutionary phylogroups with high genetic variability and diversity for LChV-1 and LChV-2 Belgian isolates, yet with little or no correlation with planting area or cultivated varieties. The global diversity and the prevalence in horticultural areas of LChV-1 and -2 variants, in association with other recently described fruit tree viruses, are of particular concern. Future epidemiological implications as well as new investigation avenues are exhaustively discussed.

High-throughput sequencing (HTS) technologies and bioinformatic analyses are of growing interest to be used as a routine diagnostic tool in the field of plant viruses. The reliability of HTS workflows from sample preparation to data analysis and results interpretation for plant virus detection and identification must be evaluated (verified and validated) to approve this tool for diagnostics. Many different extraction methods, library preparation protocols, and sequence and bioinformatic pipelines are available for virus sequence detection. To assess the performance of plant virology diagnostic laboratories in using the HTS of ribosomal RNA depleted total RNA (ribodepleted totRNA) as a diagnostic tool, we carried out an interlaboratory comparison study in which eight participants were required to use the same samples, (RNA) extraction kit, ribosomal RNA depletion kit, and commercial sequencing provider, but also their own bioinformatics pipeline, for analysis. The accuracy of virus detection ranged from 65% to 100%. The false-positive detection rate was very low and was related to the misinterpretation of results as well as to possible cross-contaminations in the lab or sequencing provider. The bioinformatic pipeline used by each laboratory influenced the correct detection of the viruses of this study. The main difficulty was the detection of a novel virus as its sequence was not available in a publicly accessible database at the time. The raw data were reanalysed using Virtool to assess its ability for virus detection. All virus sequences were detected using Virtool in the different pools. This study revealed that the ribodepletion target enrichment for sample preparation is a reliable approach for the detection of plant viruses with different genomes. A significant level of virology expertise is needed to correctly interpret the results. It is also important to improve and complete the reference data