High-throughput sequencing (HTS) technologies have become indispensable tools assisting plant virus diagnostics and research thanks to their ability to detect any plant virus in a sample without prior knowledge. As HTS technologies are heavily relying on bioinformatics analysis of the huge amount of generated sequences, it is of utmost importance that researchers can rely on efficient and reliable bioinformatic tools and can understand the principles, advantages, and disadvantages of the tools used. Here, we present a critical overview of the steps involved in HTS as employed for plant virus detection and virome characterization. We start from sample preparation and nucleic acid extraction as appropriate to the chosen HTS strategy, which is followed by basic data analysis requirements, an extensive overview of the in-depth data processing options, and taxonomic classification of viral sequences detected. By presenting the bioinformatic tools and a detailed overview of the consecutive steps that can be used to implement a well-structured HTS data analysis in an easy and accessible way, this paper is targeted at both beginners and expert scientists engaging in HTS plant virome projects.

High-throughput sequencing (HTS) technologies have brought tremendous improvements in the ability to detect plant viruses and have great potential for application in virus routine diagnostics. The performance criteria of an HTS test need therefore to be estimated and compared with traditional virus indexing tests before it can be used in routine diagnostics. In this study, 78 Musa accessions previously indexed for viruses by molecular tests and/or electron microscopy were tested individually or in pools using an HTS protocol based on total RNA sequencing. The analytical sensitivity of HTS and RT-PCR was also compared by independent testing on serial dilutions of RNA extracts. In total, 136 libraries were sequenced in five batches, and the sequences were analyzed for virus detection. The external alien control, a wheat sample infected by barley yellow dwarf virus, monitored the contamination burden and determined an adaptative detection threshold. Overall, the HTS test displayed a better analytical sensitivity than the RT-PCR and a better inclusivity than the classical indexing protocol, as distant isolates and new viral species were only detected by the HTS test. The repeatability and reproducibility of virus detection were both 100%, although differences in number of sequencing reads per virus were observed between replicates. The diagnostic sensitivity was very high, but false positive results were observed. Finally, the results also underlined the need for expert judgement in the interpretation of the results. In conclusion, the HTS test with an alien control and completed by expert evaluation fulfilled the criteria of the virus indexing protocol for Musa germplasm. [ ] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .

Saffron (Crocus sativus L.) is a vegetatively propagated crop of high economic and cultural value, potentially affected by viral infections that may impact its productivity. Despite Iran’s dominance in global saffron production, knowledge of its virome remains limited. In this study, we conducted the first nationwide virome survey of saffron in Iran employing a high-throughput sequencing (HTS) approach on pooled samples obtained from eleven provinces in Iran and one location in Afghanistan. Members of three virus families were detected—Potyviridae (Potyvirus), Solemoviridae (Polerovirus), and Geminiviridae (Mastrevirus)—as well as one satellite from the family Alphasatellitidae (Clecrusatellite). A novel Potyvirus, tentatively named saffron Iran virus (SaIRV) and detected in three provinces, shares less than 68% nucleotide identity with known Potyvirus species, thus meeting the ICTV criteria for designation as a new species. Genetic diversity analyses revealed substantial intrapopulation SNP variation but no clear geographical clustering. Among the two wild Crocus species sampled, only Crocus speciosus harbored turnip mosaic virus. Virome network and phylogenetic analyses confirmed widespread viral circulation likely driven by corm-mediated propagation. Our findings highlight the need for targeted certification programs and biological characterization of key viruses to mitigate potential impacts on saffron yield and quality.

Background High Throughput sequencing (HTS) technologies completed by the bioinformatic analysis of the generated data are becoming an important detection technique for virus diagnostics. They have the potential to replace or complement the current PCR-based methods thanks to their improved inclusivity and analytical sensitivity, as well as their overall good repeatability and reproducibility. Cross-contamination is a well-known phenomenon in molecular diagnostics and corresponds to the exchange of genetic material between samples. Cross-contamination management was a key drawback during the development of PCR-based detection and is now adequately monitored in routine diagnostics. HTS technologies are facing similar difficulties due to their very high analytical sensitivity. As a single viral read could be detected in millions of sequencing reads, it is mandatory to fix a detection threshold that will be influenced by cross-contamination. Cross-contamination monitoring should therefore be a priority when detecting viruses by HTS technologies. Results We present Cont-ID, a bioinformatic tool designed to check for cross-contamination by analysing the relative abundance of virus sequencing reads identified in sequence metagenomic datasets and their duplication between samples. It can be applied when the samples in a sequencing batch have been processed in parallel in the laboratory and with at least one external alien control. Using 273 real datasets, including 68 virus species from different hosts (fruit tree, plant, human) and several library preparation protocols (Ribodepleted total RNA, small RNA and double stranded RNA), we demonstrated that Cont-ID classifies with high accuracy (91%) viral species detection into (true) infection or (cross) contamination. This classification raises confidence in the detection and facilitates the downstream interpretation and confirmation of the results by prioritising the virus detections that should be confirmed. Conclusions Cross-contamination between samples when detecting viruses using HTS can be monitored and highlighted by Cont-ID (provided an alien control is present). Cont-ID is based on a flexible methodology relying on the output of bioinformatics analyses of the sequencing reads and considering the contamination pattern specific to each batch of samples. The Cont-ID method is adaptable so that each laboratory can optimise it before its validation and routine use.

Pre-trained language models promise to enrich analyses of single-cell data with additional layers of information leveraging large text corpora. Yet, it is still unclear how to achieve optimal alignment with the primary quantitative single-cell data. To address this, we construct text-based training datasets from both scRNA-seq data and biomedical literature targeted to the experimental setting at hand. We then jointly train language models on both information sources to learn a common, literature-enriched representation. Our examples on functionality, disease associations, and temporal trajectories show the potential of knowledge-augmented embeddings as a generalizable and interpretable strategy for enriching single-cell analysis pipelines.

A virome survey of banana plantations and their surrounding plants was carried out at nation-wide level in Malawi using virion associated nucleic acids (VANA) high throughput sequencing (HTS) on pooled samples and appropriate alien controls. In total, 366 plants were sequenced, and 23 plant virus species were detected, three species on banana (275 plants) and 20 species in surrounding plants (91 plants). Two putative novel virus species; ginger tymo-like virus and pepper derived totivirus were detected and confirmed by RT-PCR on ginger and pepper. Nine known virus species and detected a host plant was identified for two of them. No viral exchange between banana and surrounding plants was observed. Results from the VANA protocol, applied to pooled banana samples, were compared with previous targeted PCR results obtained from individual banana samples. HTS test detected better BanMMV than IC-(RT)-PCR on individual samples (better inclusivity) but detected with much lower sensitivity BBTV and BSV species, often with less than 10 reads per sample. Detection of novel and known viruses and new host plants calls for strengthened sanitory and phytosanitory measures within and beyond banana production systems. Our research confirms that HTS sensitivity depends on sampling, pooling protocol and targeted virus species.