The developmental transcriptome atlas of the biofuel crop <i>Camelina sativa</i>
Bibliographic record
Abstract
Camelina sativa is currently being embraced as a viable industrial bio-platform crop due to a number of desirable agronomic attributes and the unique fatty acid profile of the seed oil that has applications for food, feed and biofuel. The recent completion of the reference genome sequence of C. sativa identified a young hexaploid genome. To complement this work, we have generated a genome-wide developmental transcriptome map by RNA sequencing of 12 different tissues covering major developmental stages during the life cycle of C. sativa. We have generated a digital atlas of this comprehensive transcriptome resource that enables interactive visualization of expression data through a searchable database of electronic fluorescent pictographs (eFP browser). An analysis of this dataset supported expression of 88% of the annotated genes in C. sativa and provided a global overview of the complex architecture of temporal and spatial gene expression patterns active during development. Conventional differential gene expression analysis combined with weighted gene expression network analysis uncovered similarities as well as differences in gene expression patterns between different tissues and identified tissue-specific genes and network modules. A high-quality census of transcription factors, analysis of alternative splicing and tissue-specific genome dominance provided insight into the transcriptional dynamics and sub-genome interplay among the well-preserved triplicated repertoire of homeologous loci. The comprehensive transcriptome atlas in combination with the reference genome sequence provides a powerful resource for genomics research which can be leveraged to identify functional associations between genes and understand the regulatory networks underlying developmental processes.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".