Next-Gen sequencing of the transcriptome of triticale
Bibliographic record
Abstract
Triticale possesses favourable agronomic attributes originating from both its wheat and rye progenitors, including high grain and biomass yields. Triticale, primarily used as animal feed in North America, is an excellent candidate for production of industrial bio-products. Little is known about the coordination of gene expression of rye and wheat genomes in this intergeneric hybrid, but significant DNA losses from the parental genomes have been reported. To clarify the regulation of gene expression in triticale, we carried out 454 sequencing of cDNAs obtained from root, leaf, stem and floral tissues in different lines of triticale and rye exhibiting different phenotypes and assembled reads into contigs. Related to the data assembly were the absence of reference genomes and the paucity of rye sequences in GenBank or other public databases. Consequently, we have sequenced cDNA libraries from roots, seedlings, leaves, floral tissues and immature seeds to facilitate the identification of triticale sequences originating from rye. To further characterize the wheat-derived cDNAs, we also developed a database close to 25,000 non-redundant full-length wheat coding sequence genes, based on existing databases and contigs that were verified against protein sequences from the grass genomes of Brachypodium distachyon, rice, sorghum and maize.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".