Analyzing the potato abiotic stress transcriptome using expressed sequence tags
Bibliographic record
Abstract
To further increase our understanding of responses in potato to abiotic stress and the potato transcriptome in general, we generated 20 756 expressed sequence tags (ESTs) from a cDNA library constructed by pooling mRNA from heat-, cold-, salt-, and drought-stressed potato leaves and roots. These ESTs were clustered and assembled into a collection of 5240 unique sequences with 3344 contigs and 1896 singleton ESTs. Assignment of gene ontology terms (GOSlim/Plant) to the sequences revealed that 8101 assignments could be made with a total of 3863 molecular function assignments. Alignment to a set of 78 825 ESTs from other potato cDNA libraries derived from root, leaf, stolon, tuber, germinating eye, and callus tissues revealed 1476 sequences unique to abiotic stressed potato leaf and root tissue. Sequences present within the 5240 sequence set had similarity to genes known to be involved in abiotic stress responses in other plant species such as transcription factors, stress response genes, and signal transduction processes. In addition, we identified a number of genes unique to the abiotic stress library with unknown function, providing new candidate genes for investigation of abiotic stress responses in potato.
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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".