QUESNEVILLE Hadi's profile
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QUESNEVILLE Hadi

  • Research Unit in Genomics-Info (URGI), INRAE, Versailles, France
  • Bioinformatics, Evolutionary genomics, Structural genomics
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Recommendation:  1

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Educational and work
Hadi Quesneville (Ph.D) has a long-standing track record in the field of bioinformatics and genomics. His research focuses on the analysis of genomes, developing methods and tools to annotate and explore repeated sequences through “omics” data analysis and data integration. He is expert in transposable elements and repeats annotation for which he leads the development of the REPET package which annotate more than 40 genomes, most of the time in the framework of large international sequencing consortium. His work contributes to understand the impact of repeats on genome and epigenome structure and evolution in many different species.

Recommendation:  1

02 Apr 2021
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Semi-artificial datasets as a resource for validation of bioinformatics pipelines for plant virus detection

Recommended by based on reviews by Alexander Suh and 1 anonymous reviewer

Toward a critical assessment of virus detection in plants

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. [1] 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)[2], 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 [3] to predict RNA structures, Critical Assessment of Function Annotation [4] to predict gene functions, Critical Assessment of Prediction of Interactions [5] to predict protein-protein interactions, Assemblathon [6] 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” [7], 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.   

References

[1] 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

[2] Critical Assessment of protein Structure Prediction” (CASP) - https://en.wikipedia.org/wiki/CASP

[3] RNA-puzzles - https://www.rnapuzzles.org

[4] Critical Assessment of Function Annotation (CAFA) - https://en.wikipedia.org/wiki/Critical_Assessment_of_Function_Annotation

[5] Critical Assessment of Prediction of Interactions (CAPI) - https://en.wikipedia.org/wiki/Critical_Assessment_of_Prediction_of_Interactions

[6] Assemblathon - https://assemblathon.org

[7] VIROMOCK challenge - https://gitlab.com/ilvo/VIROMOCKchallenge

avatar

QUESNEVILLE Hadi

  • Research Unit in Genomics-Info (URGI), INRAE, Versailles, France
  • Bioinformatics, Evolutionary genomics, Structural genomics
  • recommender

Recommendation:  1

Reviews:  0

Educational and work
Hadi Quesneville (Ph.D) has a long-standing track record in the field of bioinformatics and genomics. His research focuses on the analysis of genomes, developing methods and tools to annotate and explore repeated sequences through “omics” data analysis and data integration. He is expert in transposable elements and repeats annotation for which he leads the development of the REPET package which annotate more than 40 genomes, most of the time in the framework of large international sequencing consortium. His work contributes to understand the impact of repeats on genome and epigenome structure and evolution in many different species.