EST sequencing and gene expression profiling of cultivated peanut (<i>Arachis hypogaea</i>L.)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Peanut (Arachis hypogaea L.) is one of the most important oil crops in the world. However, biotechnological based improvement of peanut is far behind many other crops. It is critical and urgent to establish the biotechnological platform for peanut germplasm innovation. In this study, a peanut seed cDNA library was constructed to establish the biotechnological platform for peanut germplasm innovation. About 17,000 expressed sequence tags (ESTs) were sequenced and used for further investigation. Among which, 12.5% were annotated as metabolic related and 4.6% encoded transcription or post-transcription factors. ESTs encoding storage protein and enzymes related to protein degradation accounted for 28.8% and formed the largest group of the annotated ESTs. ESTs that encoded stress responsive proteins and pathogen-related proteins accounted for 5.6%. ESTs that encoded unknown proteins or showed no hit in the GenBank nr database accounted for 20.1% and 13.9%, respectively. A total number of 5066 EST sequences were selected to make a cDNA microarray. Expression analysis revealed that these sequences showed diverse expression patterns in peanut seeds, leaves, stems, roots, flowers, and gynophores. We also analyzed the gene expression pattern during seed development. Genes that were upregulated (≥twofold) at 15, 25, 35, and 45 days after pegging (DAP) were found and compared with 70 DAP. The potential value of these genes and their promoters in the peanut gene engineering study is discussed.
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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 | 0.000 | 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 it