Promoting a functional and comparative understanding of the conifer genome- implementing applied aspects for more productive and adapted forests (ProCoGen)
Notice bibliographique
Résumé
In the midst of a climatic change scenario, the genetics of adaptive response in conifers becomes essential to ensure a sustainable management of genetic resources and an effective breeding. Conifers are the target of major tree breeding efforts worldwide. Advances in molecular technologies, such as next-generation DNA sequencing technologies, could have an enormous impact on the rate of progress and achievements made by tree breeding programmes. These new technologies might be used not only to improve our understanding of fundamental conifer biology, but also to address practical problems for the forest industry as well as problems related to the adaptation and management of conifer forests. In this context, the FP7-KBBE-2011-5 project “Promoting a functional and comparative understanding of the conifer genome- implementing applied aspects for more productive and adapted forests” (ProCoGen), granted in 2011 by the European Commission, will address genome sequencing of two keystone European conifer species. Genome re-sequencing approaches will be used to obtain two reference pine genomes. Comparative genomics and genetic diversity will be closely integrated and linked to targeted functional genomics investigations to identify genes and gene networks that efficiently help to develop or enhance applications related to forest productivity, forest stewardship in response to environmental change or conservation efforts. The development of high-throughput genotyping tools will produce an array of pre-breeding tools to be implemented in forest tree breeding programmes. ProCoGen will also develop comparative studies based on orthologous sequences, genes and markers, which will allow guiding re-sequencing initiatives and exploiting the research accumulated on each of the species under consideration to accelerate the use of genomic tools in diverse species. ProCoGen will integrate fragmented activities developed by European research groups involved in several ongoing international conifer genome initiatives and contribute to strengthening international collaboration with North American initiatives (US and Canada). Partners involved in this project are: Carmen Diaz-Sala (financial and administrative coordinator, Universidad de Alcala, UAH, Spain) Maria-Teresa Cervera (scientific coordinator, Instituto Nacional de Investigacion y Tecnologia Agraria y Alimentaria, INIA-CIFOR, also including Toni Gabaldon from Centro de Regulacion Genomica, CRG; Alvaro Soto from Universidad Politecnica de Madrid, UPM, and Isabel Arrillaga from Universidad de Valencia, UV, Spain) Francisco Canovas (Universidad de Malaga, UMA, Spain) Leopoldo Sanchez, Catherine Bastian and Christophe Plomion (Institut National de la Recherche Agronomique, INRA, France) Luc Harvengt (Institut Technologique Foret Cellulose Boisconstruction Ameublement, FCBA, France) Par Ingvarsson (Umea University, UMU, also including Sara Von Arnold from Swedish University of Agricultural Sciences, SLU, Sweden) Yves Van de Peer (Flanders Institute for Biotechnology, VIB, Belgium) Berthold Heinze (Federal Research and Training Centre for Forests, Natural Hazards and Landscape, BFW, Austria) Outi Savolainen (University of Oulu, UOULU, Finland) Giovanni G. Vendramin (Italian National Research Council, CNR-Firenze, Italy) Celia Miguel (Instituto de Biologia Experimental e Tecnologica, IBET, also including Jorge Paiva from Forest Center of the Tropical Research Institute, Portugal) John Woolliams (University of Edinburgh, UEDIN, United Kingdom) Marco Bink (Stichting Dienst Landbouwkundig Onderzoek, DLO, The Netherlands) Carl Gunnar Fossdal (Norwegian Forest and Landscape Institute, NFLI, Norway) David Torrents (Barcelona Supercomputing Center, BSC, Spain) Steve Lee (Forest Research, FR, United Kingdom) John MacKay (Universite Laval, ULaval, Canada) Kermit Ritland (University of British Columbia, UBC, Canada) Jeffrey Dean (University of Georgia, UGA, US) Daniel Peterson (Mississippi State University, MS State, US).
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 | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 ».