De novo transcriptome assembly and candidate gene discovery for high seedling vigor in cicer milkvetch ( <i>Astragalus cicer</i> L.)
Bibliographic record
Abstract
Abstract Cicer milkvetch ( Astragalus cicer L.) (CMV) is a perennial, winter hardy forage legume for grazing in the temperate regions of world. Low seed germination and low seedling vigor of this species often reduce stand establishment success, limiting producer adoption. Objectives of this study were to identify the differentially expressed genes (DEGs) associated with seedling vigor in high‐vigor (AC Veldt and PI440143) and low‐vigor populations (PI206405) in CMV. Five, 7‐day‐old seedlings per population were randomly chosen for RNA extraction. Sequencing generated a total of 319 million clean reads, with 97.3% mapped to the de novo assembled transcriptome. A total of 3,322 DEGs were identified between the high and low seedling vigor populations. Of these, a total of 269 DEGs were mapped to the UniProt database, with 30 DEGs (11.2%) aligned to species in the Fabaceae family, and 146 DEGs (54.6%) with proteins associated with seedling vigor. A total of 427 significant gene ontology (GO) terms were identified in the high versus low seedling populations, with 366 terms successfully classified into three main GO categories: molecular function, biological process, and cellular component. Among these, 286 (70.0%) GO terms were associated with seedling vigor, which were mainly related to BPs related to seed size determination, energy provision, and the transport of energy and ion. Once validated, these candidate genes could be used to select plants with high seedling vigor in CMV genetic improvement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".