Abstract
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.
Highlights
High-throughput sequencing (HTS) technologies have become an integral part of research and diagnostics toolbox in life sciences, including phytopathology and plant virology [1]
We provide a checklist, identifying the most important considerations, which should be taken into account when analyzing HTS data (Figure 3)
We aimed to provide an informative primer on the generation and analysis of HTS data for the detection of plant viruses
Summary
High-throughput sequencing (HTS) technologies have become an integral part of research and diagnostics toolbox in life sciences, including phytopathology and plant virology [1]. Many different bioinformatics tools have been developed and different pipelines have been used to detect and identify plant viruses represented in HTS datasets. We aim to present the different and often complementary approaches used for analysis of HTS data for the detection of plant viruses. We provide a short introduction to the laboratory work required and describe the possible steps in data processing for the detection of plant viruses, including quality control and trimming of the sequences, de novo assembly, sequence similarity searches, and taxonomic classification of the identified viral sequences. By including a short glossary (Figure 1), checklists, and comparison tables, we aim to present the topic to the widest possible audience and encourage the use of HTS technologies by researchers with limited experience in the field
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