Screening and identification of seed-specific genes using digital differential display tools combined with microarray data from common wheat
Bibliographic record
Abstract
BACKGROUND: Wheat is one of the most important cereal crops for human beings, with seeds being the tissue of highly economic value. Various morphogenetic and metabolic processes are exclusively associated with seed maturation. The goal of this study was to screen and identify genes specifically expressed in the developing seed of wheat with an integrative utilization of digital differential display (DDD) and available online microarray databases. RESULTS: A total of 201 unigenes were identified as the results of DDD screening and microarray database searching. The expressions of 6 of these were shown to be seed-specific by qRT-PCR analysis. Further GO enrichment analysis indicated that seed-specific genes were mainly associated with defense response, response to stress, multi-organism process, pathogenesis, extracellular region, nutrient reservoir activity, enzyme inhibitor activity, antioxidant activity and oxidoreductase activity. A comparison of this set of genes with the rice (Oryza sativa) genome was also performed and approximately three-fifths of them have rice counterparts. Between the counterparts, around 63% showed similar expression patterns according to the microarray data. CONCLUSIONS: In conclusion, the DDD screening combined with microarray data analysis is an effective strategy for the identification of seed-specific expressed genes in wheat. These seed-specific genes screened during this study will provide valuable information for further studies about the functions of these genes in wheat.
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 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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".