Skip to Main content Skip to Navigation
Preprints, Working Papers, ...

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

Abstract : 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).
Complete list of metadata

https://hal.inrae.fr/hal-03369187
Contributor : Muriel Gauthier Connect in order to contact the contributor
Submitted on : Thursday, October 7, 2021 - 10:56:35 AM
Last modification on : Friday, October 8, 2021 - 3:41:36 AM

File

2021-Tamisier-preprint.pdf
Files produced by the author(s)

Identifiers

Collections

Citation

Lucie Tamisier, Annelies Haegeman, Yoika Foucart, Nicolas Fouillien, Maher Al Rwahnih, et al.. Semi-artificial datasets as a resource for validation of bioinformatics pipelines for plant virus detection. 2021. ⟨hal-03369187⟩

Share

Metrics

Record views

57

Files downloads

19