The use of antibiotics is not permitted in Switzerland; moreover, no other medication exists to combat European foul brood. Hives with symptoms must therefore be destroyed in order to limit the outbreak, and the contaminated material must be sanitised. Since prevention is the best cure, early detection is desirable. Numerous studies have been undertaken and documents have been created at the Swiss Bee Research Centre and elsewhere to draw the attention of beekeepers to these problems and inform them about the causes and the control measures to be implemented.
Tamisier L., Haegeman A., Foucart Y., Fouillien N., Al Rwahnih M., Buzkan N., Candresse T., Chiumenti M., De Jonghe K., Lefebvre N., Margaria P., Reynard J.-S., Stevens K., Kutnjak D., Massart S.
Semi-artificial datasets as a resource for validation of bioinformatics pipelines for plant virus detection.
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).
