TAMISIER, Lucie; HAEGEMAN, Annelies; FOUCART, Yoika; FOUILLIEN, Nicolas; RWAHNIH, Maher Al; BUZKAN, Nihal; CANDRESSE, Thierry; CHIUMENTI, Michela; JONGHE, Kris De; LEFEBVRE, Marie; MARGARIA, Paolo; REYNARD, Jean-Sébastien; STEVENS, Kristian; KUTNJAK, Denis

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TAMISIER, Lucie
Gembloux Agro-Bio Tech [Faculté universitaire des sciences agronomiques de Gembloux] [[FUSAGx]]

HAEGEMAN, Annelies
Research Institute for Agricultural, Fisheries and Food [ILVO]

FOUCART, Yoika
Research Institute for Agricultural, Fisheries and Food [ILVO]

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Autre communication scientifique (congrès sans actes - poster - séminaire...)

Ce document a été publié dans

International Advances in Plant Virology, 2021-04-20, Avignon (en ligne).

Résumé en anglais

In the last decade, High-Throughput Sequencing (HTS) has revolutionized plant virus discovery and diagnosis. Currently, 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.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 (i.e. low viral concentration, new viral species, non-complete virus genome, etc). 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, we hope to encourage virologists, diagnosticians and bioinformaticians to evaluate and benchmark their pipeline(s).< Réduire

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Unités de recherche

Biologie du Fruit & Pathologie (BFP) - UMR 1332