Identification of Differentially Expressed Genes in Shoot Apex of Garlic (Allium sativum L.) Using Illumina Sequencing
Bibliographic record
Abstract
Garlic is widely used as a spice throughout the world. In this study, transcriptional profilings of garlic shoot apex were performed by using the Illumina technology. A total of 45,363 significantly changed expressed transcripts were detected between the dormant and sprouting garlic shoot apex libraries. The expression of 22,836 unigenes was increased by more than 2-fold in sprouting garlic shoot apex as compared with dormant shoot apex (up-regulated unigene), and 22,526 unigenes were identified as having been down-regulated. Gene ontology (GO) annotations indicated that the differentially expressed genes were mainly played role in nucleotide binding, plastid, hydrolase activity, transferase activity, protein metabolic process, nucleic acid binding, protein binding, mitochondrion. A total of 8,725 differentially expressed genes were assigned to five Kyoto Encyclopedia of Genes and Genomes (KEGG) biochemical pathways, including metabolism, genetic information processing, organism system, cellular processes, and environmental information processing. Real-time quantitative RT-PCR (qRT-PCR) was performed to dissect shoot apex sprouting. The differential expression of genes, such as ENHYDROUS, DAG1, DAM, DTH8, indicate that they play a critical role in shoot apex sprouting. These differentially expressed genes comprise related candidates for shoot apex sprouting regulation in Allium species.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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 | 0.001 |
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".