PubMed HealthSearch

PubMed · 41157559

Characterization of Novel Luteoviruses in Canadian Highbush Blueberries Using High-Throughput Sequencing.

Abstract

The Fraser Valley of British Columbia, Canada is among the top ten blueberry producing regions globally. Viral diseases are established in the region and significantly reduce average yields. While testing for two viruses is routine, characterization of all the viruses present in the region is incomplete. We used high-throughput sequencing to obtain an unbiased overview of RNA viruses present in 97 plants collected across the region. In addition to known viruses, we identified four luteoviruses previously unidentified in the region. Two of them matched the blueberry virus L (BlVL) and blueberry virus M (BlVM). recently found in the USA, while the third constitutes a new major variant of BlVM (BlVM-2), and the fourth a new luteovirus, which we named blueberry virus N (BlVN). The genome sequences were ~5 kbp long and contained four open-reading frames similar to other luteoviruses. PCR screening revealed that these luteoviruses are widespread in the region, and that plants typically harbour more than one of these luteoviruses. While luteoviruses are typically vectored by aphids, they were also present in nursery stock, indicating that spread also occurs via vegetative propagation.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Sachithrani Kannangara, Adam Gilewski, Juan Rodriguez Lopez, Gertruida de Villiers, Meghan Ellis, Peter Ellis, Eric Gerbrandt, Jim Mattsson. 2025-09-23. Characterization of Novel Luteoviruses in Canadian Highbush Blueberries Using High-Throughput Sequencing.. https://doi.org/10.3390/v17101286

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

Systematic performance evaluation and application validation of an end-to-end NGS workstation.

Next-generation sequencing (NGS) library preparation is a core component of precision genomics, but it is commonly constrained by inefficiency, variability, and low throughput of manual protocols. To address these limitations, we developed and systematically evaluated a fully automated NGS workstations and further validated its performance across representative application scenarios. The automated system reduced total processing time from 8 to 10 to 4–6 h. At the same time, it maintained similar performance in pre-library metric, including DNA yield and fragment size, as well as post-capture sequencing metrics (Q30 > 90%, mapping rates > 95%, on-target rates 85–90%). The duplication rate was reduced to 5–8%, compared with 10–15% for manual methods, indicating increased library complexity. Bioinformatic evaluation of inter-species read mapping showed minimal cross-contamination, with a maximum contamination ratio of 0.0003%, indicating effective sample isolation in the automated workflow. High concordance in variant detection was observed between automated and manual workflows. Overall, this automated workstation provides a standardized and reproducible workflow that supports scalable precision genomics applications.

High-Throughput Nucleotide Sequencing

RUMINA: high-throughput deduplication of unique molecular identifiers for amplicon and whole-genome sequencing with enhanced error correction.

MOTIVATION: Unique molecular identifiers (UMIs) are widely used in next-generation sequencing to enable accurate molecular counting and error correction. However, challenges remain in accurately collapsing UMI clusters, especially when read counts are low or sparse read clusters arise from barcode sequencing errors. RESULTS: We present RUMINA, a Rust-based pipeline for UMI-aware deduplication and error correction, optimized for both amplicon and shotgun sequencing. RUMINA supports multiple UMI cluster strategies, alongside majority-rule read selection independent of mapping quality, as well as discrete handling of 1-2 read clusters, paired-end merging, and read-length stratification. Benchmarking using simulated HIV population sequencing data and real-world iCLIP and TCR datasets showed that RUMINA improves ultra-low frequency SNV detection (0.01%-1%), reduces false positives, enhances reproducibility, and processes sequencing data up to 10-fold faster than existing tools. By integrating UMI- and sequence-level correction in a high-performance framework, RUMINA offers a fast, scalable, and robust solution for UMI-enabled sequencing workflows. AVAILABILITY AND IMPLEMENTATION: RUMINA is implemented in Rust and distributed as open-source code and precompiled binaries. Source code and installation instructions are available at https://github.com/greninger-lab/rumina. Documentation associated with this manuscript is available at https://github.com/greninger-lab/rumina_paper.

High-Throughput Nucleotide Sequencing

Enzymes in high-throughput RNA sequencing: Applications and challenges.

High-throughput RNA sequencing provides genome-wide information on the dynamics of RNA in each cell and how the dynamics responds to environmental changes. Next-generation sequencing by the Illumina platform currently provides the highest information output as compared to other platforms. A key component of next generation sequencing of each RNA is the successful end-to-end reverse-transcription into a cDNA strand. This can be highly challenging given the propensity of each RNA to adopt ordered structures and to contain post-transcriptional modifications. While many reverse transcriptase (RT) enzymes have been developed over the years to maximize read-through of an RNA, their processivity and efficiency varies, raising the question of how to select the RT for the experiment at hand. Here, we use tRNA as a model for genome-wide sequencing, as tRNA has a stable secondary and tertiary structure and has a high density and wide variety of post-transcriptional modifications, presenting one of the most challenging problems of sequencing RNA. We compare the efficiency of end-to-end cDNA synthesis of tRNA among several recent RT enzymes and provide a general sequencing workflow that is applicable to most of these enzymes.

High-Throughput Nucleotide Sequencing