Kodoja: A workflow for virus detection in plants using k-mer analysis of RNA-sequencing data

Baizan-Edge, Amanda; Cock, Peter; MacFarlane, Stuart A.; McGavin, W.J.; Torrance, L.; Jones, Susan R.

doi:10.1099/jgv.0.001210

Cited by 13 publications

(14 citation statements)

References 40 publications

(50 reference statements)

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“…Four bioinformatics approaches were executed on the ribodepleted RNA-Seq samples to detect viruses and viroids. We trialed two methods that use direct annotation on quality trimmed reads without any assembly: the plant virus detection pipeline (PVDP) [ 32 ] and Kodoja [ 33 ]. These workflows were run on default parameters using the pre-computed database kodojaDB_v1.0 and the PlantVirusesDB_0420v4_masked.fa for Kodoja and PVDP, respectively.…”

Section: Methodsmentioning

confidence: 99%

Side-by-Side Comparison of Post-Entry Quarantine and High Throughput Sequencing Methods for Virus and Viroid Diagnosis

Gauthier

Lelwala

Elliott³

et al. 2022

Biology

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Rapid and safe access to new plant genetic stocks is crucial for primary plant industries to remain profitable, sustainable, and internationally competitive. Imported plant species may spend several years in Post Entry Quarantine (PEQ) facilities, undergoing pathogen testing which can impact the ability of plant industries to quickly adapt to new global market opportunities by accessing new varieties. Advances in high throughput sequencing (HTS) technologies provide new opportunities for a broad range of fields, including phytosanitary diagnostics. In this study, we compare the performance of two HTS methods (RNA-Seq and sRNA-Seq) with that of existing PEQ molecular assays in detecting and identifying viruses and viroids from various plant commodities. To analyze the data, we tested several bioinformatics tools which rely on different approaches, including direct-read, de novo, and reference-guided assembly. We implemented VirusReport, a new portable, scalable, and reproducible nextflow pipeline that analyses sRNA datasets to detect and identify viruses and viroids. We raise awareness of the need to evaluate cross-sample contamination when analyzing HTS data routinely and of using methods to mitigate index cross-talk. Overall, our results suggest that sRNA analyzed using VirReport provides opportunities to improve quarantine testing at PEQ by detecting all regulated exotic viruses from imported plants in a single assay.

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Section: Methodsmentioning

confidence: 99%

Side-by-Side Comparison of Post-Entry Quarantine and High Throughput Sequencing Methods for Virus and Viroid Diagnosis

Gauthier

Lelwala

Elliott³

et al. 2022

Biology

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“…Even if most of the k-mer-based classification tools, such as Kraken [103,104], Kaiju [105], or Taxonomer [106], are not dedicated toward the detection of plant viruses, they can be used for such purpose. Kodoja [107] uses a combination of such tools for the taxonomic classification of plant viruses in metagenomic data. Most of the tools are not very user friendly, and the use of k-mer tools for plant virus detection is fairly new; thus, some questions remain to be answered, e.g., the usability of k-mer tools on small RNA datasets [107].…”

Section: K-mer Approaches and Machine Learning-based Approachesmentioning

confidence: 99%

“…Kodoja [107] uses a combination of such tools for the taxonomic classification of plant viruses in metagenomic data. Most of the tools are not very user friendly, and the use of k-mer tools for plant virus detection is fairly new; thus, some questions remain to be answered, e.g., the usability of k-mer tools on small RNA datasets [107].…”

Section: K-mer Approaches and Machine Learning-based Approachesmentioning

confidence: 99%

A Primer on the Analysis of High-Throughput Sequencing Data for Detection of Plant Viruses

et al. 2021

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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.

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“…quality filtering/trimming, adapter removal, optional merging of forward and reverse reads), an optional plant host removal and/or assembly step, taxonomic classification of reads or contigs (mapping, sequence/domain similarity searches or k-mer based approaches against virus or more general databases) and finally -if necessary -haplotype reconstruction. Dedicated software combining all analyses steps exist, such as VirAnnot (Lefebvre et al, 2019), Virusdetect (Zheng et al, 2017), Virfind (Ho & Tzanetakis, 2014), Virtool (Rott et al, 2017), IDseq (Kalantar et al, 2020), Galaxy (Afgan et al, 2018 with for example Kodoja as plug-in (Baizan-Edge et al, 2019), Truffle (Visser et al, 2016), but also more general commercial software, such as CLC Genomics Workbench and Geneious Prime. Most of them aim to improve virus detection and/or reduce processing time, but the high number of pipelines available complicate the choice of the most appropriate for a given goal or environment.…”

Section: Introductionmentioning

confidence: 99%

Semi-artificial datasets as a resource for validation of bioinformatics pipelines for plant virus detection

Tamisier¹,

Haegeman²,

Foucart³

et al. 2021

Peer Community Journal

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The widespread use of High-Throughput Sequencing (HTS) for detection of plant viruses and sequencing of plant virus genomes has led to the generation of large amounts of data and of bioinformatics challenges to process them. Many bioinformatics pipelines for virus detection are available, making the choice of a suitable one difficult. A robust benchmarking is needed for the unbiased comparison of the pipelines, but there is currently a lack of reference datasets that could be used for this purpose. We present 7 semi-artificial datasets composed of real RNA-seq datasets from virus-infected plants spiked with artificial virus reads. Each dataset addresses challenges that could prevent virus detection. We also present 3 real datasets showing a challenging virus composition as well as 8 completely artificial datasets to test haplotype reconstruction software. With these datasets that address several diagnostic challenges, we hope to encourage virologists, diagnosticians and bioinformaticians to evaluate and benchmark their pipeline(s).

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Kodoja: A workflow for virus detection in plants using k-mer analysis of RNA-sequencing data

Abstract: Repositories: The RNA sequences of 2 raspberry plants exhibiting virus-like symptoms have been deposited in the European Nucleotide Archive and assigned accessions ERR2784286 (D5) and ERR2784287(D6)

Cited by 13 publications

References 40 publications

Side-by-Side Comparison of Post-Entry Quarantine and High Throughput Sequencing Methods for Virus and Viroid Diagnosis

Side-by-Side Comparison of Post-Entry Quarantine and High Throughput Sequencing Methods for Virus and Viroid Diagnosis

A Primer on the Analysis of High-Throughput Sequencing Data for Detection of Plant Viruses

Semi-artificial datasets as a resource for validation of bioinformatics pipelines for plant virus detection

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