In-depth transcriptome characterization uncovers distinct gene family expansions for Cupressus gigantea important to this long-lived species’ adaptability to environmental cues
Bibliographic record
Abstract
BACKGROUND: Cupressus gigantea, a rare and endangered tree species with remarkable medicinal value, is endemic to the Tibetan Plateau. Yet, little is known about the underlying genetics of the unique ecological adaptability of this extremely long-lived conifer with a large genome size. Here, we present its first de novo and multi-tissue transcriptome in-depth characterization. RESULTS: We performed Illumina paired-end sequencing and RNA libraries assembly derived from terminal buds, male and female strobili, biennial leaves, and cambium tissues taken from adult C. gigantea. In total, large-scale high-quality reads were assembled into 101,092 unigenes, with an average sequence length of 1029 bp, and 6848 unigenes (6.77%) were mapped against the KEGG databases to identify 292 pathways. A core set of 41,373 genes belonging to 2412 orthologous gene families shared between C. gigantea and nine other plants was revealed. In addition, we identified 2515 small to larger-size gene families containing in total 9223 genes specific to C. gigantea, and enriched for gene ontologies relating to biotic interactions. We identified an important terpene synthases gene family expansion with its 121 putative members. CONCLUSIONS: This study presents the first comprehensive transcriptome characterization of C. gigantea. Our results will facilitate functional genomic studies to support genetic improvement and conservation programs for this endangered conifer.
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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) | 0.000 | 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 | 0.000 |
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".