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Record W4200131701 · doi:10.21203/rs.3.rs-1198968/v1

Reference genome-assisted identification of stem rust resistance gene Sr62 encoding a tandem kinase

2021· preprint· en· W4200131701 on OpenAlexaff
Guotai Yu, Oadi Matny, Nicolas Champouret, Burkhard Steuernagel, Matthew Moscou, Inmaculada Hernández‐Pinzón, Phon Green, Şadiye Hayta, Mark A. Smedley, Wendy Harwood, Ngonidzashe Kangara, Yajuan Yue, Catherine Gardener, Mark J. Banfield, Pablo D. Olivera, Cole Welchin, Jamie Simmons, E. Millet, Anna Minz‐Dub, Moshe Ronen, Raz Avni, Amir Sharon, Mehran Patpour, Annemarie Fejer Justesen, Murukarthick Jayakodi, Axel Himmelbach, Nils Stein, Shuangye Wu, Jesse Poland, Jennifer Ens, Curtis Pozniak, Miroslava Karafiátová, István Molnár, Jaroslav Doležel, E. R. Ward, T. Lynne Reuber, Jonathan D. G. Jones, Martin Mascher, Brian J. Steffenson, Brande B. H. Wulff

Bibliographic record

VenueResearch Square · 2021
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicWheat and Barley Genetics and Pathology
Canadian institutionsUniversity of Saskatchewan
FundersBiotechnology and Biological Sciences Research CouncilTel Aviv UniversityGatsby Charitable FoundationDirectorate for Biological SciencesUniversity of MinnesotaGordon and Betty Moore Foundation
KeywordsStem rustBiologyGeneticsGeneGenomePlant disease resistanceComputational biology

Abstract

fetched live from OpenAlex

Abstract The wild relatives and progenitors of wheat have been widely used as sources of disease resistance (R) genes. Molecular identification and characterization of these R genes facilitates their manipulation and tracking in breeding programmes. We developed a reference-quality genome assembly of the wild diploid wheat relative Aegilops sharonensis and used positional mapping, mutagenesis, RNA-Seq and transgenesis to identify the stem rust resistance gene Sr62, which was also transferred to common wheat. This gene encodes a tandem kinase, homologues of which exist across multiple taxa in the plant kingdom. Stable Sr62 transgenic wheat lines showed high levels of resistance against diverse isolates of the stem rust pathogen, highlighting the utility of Sr62 for deployment as part of a polygenic stack to maximize the durability of stem rust resistance.

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)0.000
metaresearch head score (Gemma)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.003

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 head0.135
GPT teacher head0.344
Teacher spread0.209 · 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

Citations10
Published2021
Admission routes1
Has abstractyes

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