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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 informed by estimated 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 specific external control called 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 (Illumina technology) 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., (© 2023. BioMed Central Ltd., part of Springer Nature.)

Cassava Brown Streak Disease (CBSD), which is caused by cassava brown streak virus (CBSV) and Ugandan cassava brown streak virus (UCBSV), represents one of the most devastating threats to cassava production in Africa, including in Rwanda where a dramatic epidemic in 2014 dropped cassava yield from 3.3 million to 900,000 tonnes (1). Studying viral genetic diversity at the genome level is essential in disease management, as it can provide valuable information on the origin and dynamics of epidemic events. To fill the current lack of genome-based diversity studies of UCBSV, we performed a nationwide survey of cassava ipomovirus genomic sequences in Rwanda by high-throughput sequencing (HTS) of pools of plants sampled from 130 cassava fields in thirteen cassava-producing districts, spanning seven agro-ecological zones with contrasting climatic conditions and different cassava cultivars. HTS allowed the assembly of a nearly complete consensus genome of UCBSV in twelve districts. The phylogenetic analysis revealed high homology between UCBSV genome sequences, with a maximum of 0.8 per cent divergence between genomes at the nucleotide level. An in-depth investigation based on Single Nucleotide Polymorphisms (SNPs) was conducted to explore the genome diversity beyond the consensus sequences. First, to ensure the validity of the result, a panel of SNPs was confirmed by independent reverse transcription polymerase chain reaction (RT-PCR) and Sanger sequencing. Furthermore, the combination of fixation index ( F ST ) calculation and Principal Component Analysis (PCA) based on SNP patterns identified three different UCBSV haplotypes geographically clustered. The haplotype 2 (H 2 ) was restricted to the central regions, where the NAROCAS 1 cultivar is predominantly farmed. RT-PCR and Sanger sequencing of individual NAROCAS1 plants confirmed their association with H 2 . Haplotype 1 was widely spread, with a 100 per cent occurrence in the Eastern region, while Haplotype 3 was only found in the Western region. These haplotypes' associations with specific cultivars or regions would need further confirmation. Our results prove that a much more complex picture of genetic diversity can be deciphered beyond the consensus sequences, with practical implications on virus epidemiology, evolution, and disease management. Our methodology proposes a high-resolution analysis of genome diversity beyond the consensus between and within samples. It can be used at various scales, from individual plants to pooled samples of virus-infected plants. Our findings also showed how subtle genetic differences could be informative on the potential impact of agricultural practices, as the presence and frequency of a virus haplotype could be correlated with the dissemination and adoption of improved cultivars., Competing Interests: The authors declare no conflict of interest., (© The Author(s) 2023. Published by Oxford University Press.)

The advances in high-throughput sequencing (HTS) technologies and bioinformatic tools have provided new opportunities for virus and viroid discovery and diagnostics. Hence, new sequences of viral origin are being discovered and published at a previously unseen rate. Therefore, a collective effort was undertaken to write and propose a framework for prioritizing the biological characterization steps needed after discovering a new plant virus to evaluate its impact at different levels. Even though the proposed approach was widely used, a revision of these guidelines was prepared to consider virus discovery and characterization trends and integrate novel approaches and tools recently published or under development. This updated framework is more adapted to the current rate of virus discovery and provides an improved prioritization for filling knowledge and data gaps. It consists of four distinct steps adapted to include a multi-stakeholder feedback loop. Key improvements include better prioritization and organization of the various steps, earlier data sharing among researchers and involved stakeholders, public database screening, and exploitation of genomic information to predict biological properties., Competing Interests: AF was employed by Fera Science Ltd. AyM and YH were employed by Abiopep S.L. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Fontdevila, Khalili, Maachi, Rivarez, Rollin, Salavert, Temple, Aranda, Boonham, Botermans, Candresse, Fox, Hernando, Kutnjak, Marais, Petter, Ravnikar, Selmi, Tahzima, Trontin, Wetzel and Massart.)

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.

Background and Purpose:   It is widely acknowledged that individual response to antiepileptic drugs (AEDs) is influenced by genetic factors. However, most of the underlying genes and genetic variants remain unidentified to date. The purpose of this study is to examine the role of common variants in a number of candidate genes in the response to commonly prescribed AEDs., Methods:   We recruited 495 patients with epilepsy. Patients were classified according to their response to several AEDs. We genotyped 104 polymorphisms in 17 candidate genes for AED response. We looked for statistically significant associations between these polymorphisms and well-defined AED response phenotypes., Results:   We identified significant associations of CYP2C9 variant alleles with presence of phenytoin (PHT) adverse drug reactions (ADRs) and of GSTM1 copy number variation with the presence of carbamazepine ADRs. The latter association could not be confirmed in a replication study., Conclusions:   Our study is the first comprehensive candidate gene association study in epilepsy pharmacogenetics. Our results confirm the role of CYP2C9 variants in PHT toxicity. No other definite associations were identified. Large-scale efforts are needed to unravel the genetic determinants of AED response., (© 2011 The Author(s). European Journal of Neurology © 2011 EFNS.)