Computational Pipeline for the Detection of Plant RNA Viruses Using High-Throughput Sequencing.

In this chapter, we describe a computational pipeline for the in silico detection of plant viruses by high-throughput sequencing (HTS) from total RNA samples. The pipeline is designed for the analysis of short reads generated using an Illumina platform and free-available software tools. First, we provide advice for high-quality total RNA purification, library preparation, and sequencing. The bioinformatics pipeline begins with the raw reads obtained from the sequencing machine and performs some curation steps to obtain long contigs. Contigs are blasted against a local database of reference nucleotide viral sequences to identify the viruses in the samples. Then, the search is refined by applying specific filters. We also provide the code to re-map the short reads against the viruses found to get information on sequencing depth and read coverage for each virus. No previous bioinformatics background is required, but basic knowledge of the Unix command line and R language is recommended. © 2024. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.

Citation

Livia Donaire, Miguel A Aranda. Computational Pipeline for the Detection of Plant RNA Viruses Using High-Throughput Sequencing. Methods in molecular biology (Clifton, N.J.). 2024;2724:1-20

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PMID: 37987894

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