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Semi-artificial datasets as a resource for validation of bioinformatics pipelines for plant virus detectionuse asterix (*) to get italics
Lucie Tamisier, Annelies Haegeman, Yoika Foucart, Nicolas Fouillien, Maher Al Rwahnih, Nihal Buzkan, Thierry Candresse, Michela Chiumenti, Kris De Jonghe, Marie Lefebvre, Paolo Margaria, Jean Sébastien Reynard, Kristian Stevens, Denis Kutnjak, Sébastien MassartPlease use the format "First name initials family name" as in "Marie S. Curie, Niels H. D. Bohr, Albert Einstein, John R. R. Tolkien, Donna T. Strickland"
<p>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.</p> should fill this box only if you chose 'All or part of the results presented in this preprint are based on data'. URL must start with http:// or https:// should fill this box only if you chose 'Scripts were used to obtain or analyze the results'. URL must start with http:// or https://
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High-Throughput Sequencing, Reference data, Semi-artificial dataset, Plant virus detection, Bioinformatics pipelines, Haplotype reconstruction
Bioinformatics, Plants, Viruses and transposable elements
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2020-11-27 14:31:47
Hadi Quesneville