EST sequencing and gene expression profiling of cultivated peanut (<i>Arachis hypogaea</i>L.)
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| 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 | 0.001 |
| 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 source (direct Gemma or distilled Codex), 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".