MétaCan
Menu
Back to cohort
Record W2765978247 · doi:10.3390/genes8110291

RNA-Seq Analysis of Plant Maturity in Crested Wheatgrass (Agropyron cristatum L.)

2017· article· en· W2765978247 on OpenAlexafffundabout
Fangqin Zeng, Bill Biligetu, Bruce Coulman, Michael P. Schellenberg, Yong‐Bi Fu

Bibliographic record

VenueGenes · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBioenergy crop production and management
Canadian institutionsAgriculture and Agri-Food CanadaUniversity of Saskatchewan
FundersAgriculture and Agri-Food Canada
KeywordsBiologyAgropyron cristatumGeneAnthesisTranscriptomeBotanyCultivarGeneticsGene expression

Abstract

fetched live from OpenAlex

Crested wheatgrass (Agropyron cristatum L.) breeding programs aim to develop later maturing cultivars for extending early spring grazing in Western Canada. Plant maturity is a complex genetic trait, and little is known about genes associated with late maturity in this species. An attempt was made using RNA-Seq to profile the transcriptome of crested wheatgrass maturity and to analyze differentially expressed genes (DEGs) between early and late maturing lines. Three cDNA libraries for each line were generated by sampling leaves at the stem elongation stage, spikes at the boot and anthesis stages. A total of 75,218,230 and 74,015,092 clean sequence reads were obtained for early and late maturing lines, respectively. De novo assembly of all sequence reads generated 401,587 transcripts with a mean length of 546 bp and N50 length of 691 bp. Out of 13,133 DEGs detected, 22, 17, and eight flowering related DEGs were identified for the three stages, respectively. Twelve DEGs, including nine flowering related DEGs at the stem elongation stage were further confirmed by qRT-PCR. The analysis of homologous genes of the photoperiod pathway revealed their lower expression in the late maturing line at the stem elongation stage, suggesting that their differential expression contributed to late maturity in crested wheatgrass.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.746
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.028
GPT teacher head0.244
Teacher spread0.216 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations7
Published2017
Admission routes3
Has abstractyes

Explore more

Same venueGenesSame topicBioenergy crop production and managementFrench-language works237,207