Book of abstracts of the Scientific Colloquium 'Plant Health at the Age of Metagenomics'
Notice bibliographique
Résumé
New techniques allowing processing of large numbers of samples and generating huge volumes of genomic and protein data offer new opportunities to study plant pests. Rather than isolating individual molecules, in order to understand their role in the biology of an organism, it is now possible to investigate the genome as a whole, or the interactions of proteins and other metabolites as a holistic approach. These new techniques allow known pests to be studied in more detail as well as to investigate the genomic diversity within species. The information produced will improve diagnostics and may help in the management of pests, for instance to develop resistant plant species. These new techniques can also detect new species that may have been present for many years unnoticed, as well as emerging pests. They may also help identify the causal agents of diseases which were previously unknown. Genomic data may show similarity between new species found and known pests, which may give indications on the relevance of these newly identified organisms as potential pests. Data needs to be stored in a way that it is findable, accessible, interoperable and reusable (FAIR). Bioinformatics is an important tool to handle and analyse the ever-growing amount of data produced for example by comparing genomic sequences, predicting the activities of proteins or modelling metabolic pathways. What is the biological significance of the data? In a plant health context, one may ask which genes, proteins or other molecules cause organisms to be pathogenic? Which molecules increase virulence? The EPPO/Euphresco Colloquium ‘Plant Health at the age of metagenomics’ will bring together scientists and regulators who are involved in plant health. By sharing experiences and information, together we can better understand the possibilities that metagenomics offers and how to use the vast amounts of information that will become available to improve protection of plant health. A crucial question is what makes a new species that is detected a threat, and warrants phytosanitary action? What in the genome, the proteins and other molecules of a pest makes it potentially invasive in a new region or a risk to a new host? If we shift the focus from pests only to the interaction between plants and pests one can ask what in a plant makes it a host, or more (or less) susceptible to damage by the pest? This colloquium is a very good opportunity to discuss the challenges associated with the study of the genome of pests, populations, and plants, and the opportunities offered to obtain information about which plants are hosts, the extent of damage the pest could cause in a host and the way in which we could mitigate the effect of the pest. In the same way that I see the benefits of the rapid developments in sequencing and bioinformatic analysis capacity, I am looking forward to the fast development of data interpretation, in particular concerning information relevant to the interaction between pests and plants and thereby to the management of pests and diseases.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,069 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».