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Record W2886785109 · doi:10.1126/science.aar7191

Shifting the limits in wheat research and breeding using a fully annotated reference genome

2018· article· en· W2886785109 on OpenAlexafffund
R. Appels, Kellye Eversole, Nils Stein, Catherine Feuillet, Beat Keller, Jane Rogers, Curtis Pozniak, Frédéric Choulet, Assaf Distelfeld, Jesse Poland, Gil Ronen, Andrew Sharpe, Omer Barad, Kobi Baruch, Gabriel Keeble‐Gagnère, Martin Mascher, Gil Ben-Zvi, Ambre-Aurore Josselin, Axel Himmelbach, François Balfourier, Juan J. Gutiérrez-González, Matthew Hayden, ChuShin Koh, Gary J. Muehlbauer, Raj Pasam, Etienne Paux, Philippe Rigault, Josquin Tibbits, Vijay Tiwari, M. Spannagl, Daniel Lang, Heidrun Gundlach, Georg Haberer, Klaus Mayer, Danara Ormanbekova, Verena M. Prade, Hana Šimková, Thomas Wicker, David Swarbreck, Hélène Rimbert, Marius Felder, Nicolas Guilhot, Gemy Kaithakottil, Jens Keilwagen, Philippe Leroy, Thomas Lux, Sven Twardziok, Luca Venturini, Angéla Juhász, Michaël Abrouk, Iris Fischer, Cristóbal Uauy, Philippa Borrill, Ricardo H. Ramírez-González, Dominique Arnaud, Smahane Chalabi, Boulos Chalhoub, Aron T. Cory, Raju Datla, Mark W. Davey, John Jacobs, Stephen J. Robinson, Burkhard Steuernagel, Fred van Ex, Brande B. H. Wulff, Moussa Benhamed, Abdelhafid Bendahmane, Lorenzo Concia, David Latrasse, Jan Bartoš, Arnaud Bellec, Hélène Bergès, Jaroslav Doležel, Zeev Frenkel, Bikram Gill, Abraham B. Korol, Thomas Letellier, Odd-Arne Olsen, Kuldeep Singh, Miroslav Valárik, Edwin van der Vossen, Sonia Vautrin, Song Weining, Tzion Fahima, Vladimir Glikson, Dina Raats, Jarmila Číhalíková, Helena Toegelová, Jan Vrána, Pierre Sourdille, Benoît Darrier, Delfina Barabaschi, Luigi Cattivelli, Pilar Hernández, Sergio Gálvez, Hikmet Budak, Jonathan D. G. Jones, Kamil Witek, Guotai Yu, Ian Small, Joanna Melonek, Ruonan Zhou, Tatiana Belova, K. Kanyuka, Robert C. King, Kirby T. Nilsen, Sean Walkowiak, Richard D. Cuthbert, R. E. Knox, Krysta Wiebe, Daoquan Xiang, Antje Rohde, T. J. Golds, Jana Čížková, Bala Anı Akpınar, Sezgi Biyiklioglu, Liangliang Gao, Amidou N’Daiye, Marie Kubaláková, Jan Šafář, Françoise Alfama, Anne‐Françoise Adam‐Blondon, Raphaël Flores, Claire Guerche, Mikaël Loaec, Hadi Quesneville, Janet Condie, Jennifer Ens, Ron MacLachlan, Yifang Tan, Adriana Alberti, Jean‐Marc Aury, Valérie Barbe, Arnaud Couloux, Corinne Cruaud, Karine Labadie, Sophie Mangenot, Patrick Wincker, Gaganpreet Kaur, Ming‐Cheng Luo, Sunish K. Sehgal, Parveen Chhuneja, O. P. Gupta, Suruchi Jindal, Parampreet Kaur, Palvi Malik, Priti Sharma, Bharat Yadav, Nisha Singh, Jitendra P. Khurana, Chanderkant Chaudhary, Paramjit Khurana, Vinod Kumar, Ajay Kumar Mahato, Saloni Mathur, Amitha Mithra Sevanthi, Naveen Sharma, Rukam S. Tomar, Kateřina Holušová, Ondřej Plíhal, Matthew D. Clark, Darren Heavens, George Kettleborough, Jon Wright, Barbora Balcárková, Yuqin Hu, Е. А. Салина, Nikolai V. Ravin, K. G. Skryabin, Alexey V. Beletsky, Vitaly V. Kadnikov, Andrey V. Mardanov, Andrey L. Rakitin, E. M. Sergeeva, Hirokazu Handa, Hiroyuki Kanamori, Satoshi Katagiri, Fuminori Kobayashi, Shuhei Nasuda, Tsuyoshi Tanaka, Federica Cattonaro, Min Jiumeng, Karl Kugler, Matthias Pfeifer, Simen R. Sandve, Xun Xu, Bujie Zhan, Jacqueline Batley, Philipp E. Bayer, David Edwards, Satomi Hayashi, Zuzana Tulpová, Paul Visendi, Licao Cui, Xianghong Du, Kewei Feng, Xiaojun Nie, Wei Tong, Le Wang

Bibliographic record

VenueScience · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicWheat and Barley Genetics and Pathology
Canadian institutionsAgriculture and Agri-Food CanadaPlant Biotechnology InstituteDefence Research and Development CanadaGlobal Institute for Water SecurityUniversity of Saskatchewan
FundersBayer CropScienceNational Institute of Food and AgricultureDirectorate for Biological SciencesSaskatchewan Wheat Development CommissionJunta de AndalucíaGenome PrairieCanada First Research Excellence FundNorges Miljø- og Biovitenskapelige UniversitetRussian Science FoundationBiotechnology and Biological Sciences Research CouncilInstitut National de la Recherche AgronomiqueUniversität ZürichNorges ForskningsrådMinistry of Agriculture, Forestry and FisheriesConsiglio per la ricerca in agricoltura e l’analisi dell’economia agrariaAustralian GovernmentAgence Nationale de la RechercheMinistry of Agriculture - SaskatchewanGenome CanadaMinisterio de Asuntos Económicos y Transformación Digital, Gobierno de EspañaDeutscher Akademischer AustauschdienstRIKENTokyo University of AgricultureCentre of Excellence in Plant Energy Biology, Australian Research CouncilGerman Network for Bioinformatics InfrastructureBundesministerium für Ernährung und LandwirtschaftLeibniz-GemeinschaftTel Aviv UniversityGrantová Agentura České RepublikyGordon and Betty Moore FoundationWestern Grains Research FoundationEuropean CommissionDeutsche ForschungsgemeinschaftGrains Research and Development CorporationU.S. Department of AgricultureCanadian Light SourceAgriculture and Agri-Food CanadaNational Science Foundation
KeywordsGenomeBiologyReference genomeComputational biologyGeneGeneticsBiotechnology

Abstract

fetched live from OpenAlex

An annotated reference sequence representing the hexaploid bread wheat genome in 21 pseudomolecules has been analyzed to identify the distribution and genomic context of coding and noncoding elements across the A, B, and D subgenomes. With an estimated coverage of 94% of the genome and containing 107,891 high-confidence gene models, this assembly enabled the discovery of tissue- and developmental stage-related coexpression networks by providing a transcriptome atlas representing major stages of wheat development. Dynamics of complex gene families involved in environmental adaptation and end-use quality were revealed at subgenome resolution and contextualized to known agronomic single-gene or quantitative trait loci. This community resource establishes the foundation for accelerating wheat research and application through improved understanding of wheat biology and genomics-assisted breeding.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.039
metaresearch head score (Gemma)0.033
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.039
Threshold uncertainty score0.208

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0390.033
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.004
Science and technology studies0.0010.002
Scholarly communication0.0050.005
Open science0.0030.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0020.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.218
GPT teacher head0.355
Teacher spread0.136 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3,376
Published2018
Admission routes2
Has abstractyes

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