Toward a critical assessment of virus detection in plants
Semi-artificial datasets as a resource for validation of bioinformatics pipelines for plant virus detection
The advent of High Throughput Sequencing (HTS) since the last decade has revealed previously unsuspected diversity of viruses as well as their (sometimes) unexpected presence in some healthy individuals. These results demonstrate that genomics offers a powerful tool for studying viruses at the individual level, allowing an in-depth inventory of those that are infecting an organism. Such approaches make it possible to study viromes with an unprecedented level of detail, both qualitative and quantitative, which opens new venues for analyses of viruses of humans, animals and plants. Consequently, the diagnostic field is using more and more HTS, fueling the need for efficient and reliable bioinformatics tools.
Many such tools have already been developed, but in plant disease diagnostics, validation of the bioinformatics pipelines used for the detection of viruses in HTS datasets is still in its infancy. There is an urgent need for benchmarking the different tools and algorithms using well-designed reference datasets generated for this purpose. This is a crucial step to move forward and to improve existing solutions toward well-standardized bioinformatics protocols. This context has led to the creation of the Plant Health Bioinformatics Network (PHBN), a Euphresco network project aiming to build a bioinformatics community working on plant health. One of their objectives is to provide researchers with open-access reference datasets allowing to compare and validate virus detection pipelines.
In this framework, Tamisier et al.  present real, semi-artificial, and completely artificial datasets, each aimed at addressing challenges that could affect virus detection. These datasets comprise real RNA-seq reads from virus-infected plants as well as simulated virus reads. Such a work, providing open-access datasets for benchmarking bioinformatics tools, should be encouraged as they are key to software improvement as demonstrated by the well-known success story of the protein structure prediction community: their pioneer community-wide effort, called Critical Assessment of protein Structure Prediction (CASP), has been providing research groups since 1994 with an invaluable way to objectively test their structure prediction methods, thereby delivering an independent assessment of state-of-art protein-structure modelling tools. Following this success, many other bioinformatic community developed similar “competitions”, such as RNA-puzzles  to predict RNA structures, Critical Assessment of Function Annotation  to predict gene functions, Critical Assessment of Prediction of Interactions  to predict protein-protein interactions, Assemblathon  for genome assembly, etc. These are just a few examples from a long list of successful initiatives. Such efforts enable rigorous assessments of tools, stimulate the developers’ creativity, but also provide user communities with a state-of-art evaluation of available tools.
Inspired by these success stories, the authors propose a “VIROMOCK challenge” , asking researchers in the field to test their tools and to provide feedback on each dataset through a repository. This initiative, if well followed, will undoubtedly improve the field of virus detection in plants, but also probably in many other organisms. This will be a major contribution to the field of viruses, leading to better diagnostics and, consequently, a better understanding of viral diseases, thus participating in promoting human, animal and plant health.
 Tamisier, L., Haegeman, A., Foucart, Y., Fouillien, N., Al Rwahnih, M., Buzkan, N., Candresse, T., Chiumenti, M., De Jonghe, K., Lefebvre, M., Margaria, P., Reynard, J.-S., Stevens, K., Kutnjak, D. and Massart, S. (2021) Semi-artificial datasets as a resource for validation of bioinformatics pipelines for plant virus detection. Zenodo, 4273791, version 4 peer-reviewed and recommended by Peer community in Genomics. doi: https://doi.org/10.5281/zenodo.4273791
 Critical Assessment of protein Structure Prediction” (CASP) - https://en.wikipedia.org/wiki/CASP
 RNA-puzzles - https://www.rnapuzzles.org
 Critical Assessment of Function Annotation (CAFA) - https://en.wikipedia.org/wiki/Critical_Assessment_of_Function_Annotation
 Critical Assessment of Prediction of Interactions (CAPI) - https://en.wikipedia.org/wiki/Critical_Assessment_of_Prediction_of_Interactions
 Assemblathon - https://assemblathon.org
 VIROMOCK challenge - https://gitlab.com/ilvo/VIROMOCKchallenge
Hadi Quesneville (2021) Toward a critical assessment of virus detection in plants. Peer Community in Genomics, 100007. 10.24072/pci.genomics.100007
Evaluation round #215 Mar 2021
DOI or URL of the preprint: https://zenodo.org/record/4273792#.YEIjC-fjKUk, https://zenodo.org/record/4584967#.YEIku-fjKUk
Version of the preprint: 10.5281/zenodo.4273792, 10.5281/zenodo.4584967
Decision by Hadi Quesneville
The responses brought by the autors to the reviewers are satisfactory. However two references in the text "Text S1" (line 117) and "Table S1" (line 125) cannot be found in the manuscript. The authors should fix this in order to have their preprint recommended.
Evaluation round #119 Jan 2021
DOI or URL of the preprint: https://zenodo.org/record/4293594#.X8D6GLPjJEY
Version of the preprint:
Decision by Hadi Quesneville
The two referees found your article interesting and potentially of great value. However, it can be still improved according to their suggestions. I recommend you to take into account their suggestions and to re-submit it for a second evaluation round.