A long-read and short-read transcriptomics approach provides the first high-quality reference transcriptome and genome annotation for <i>Pseudotsuga menziesii</i> (Douglas-fir)
Bibliographic record
Abstract
Douglas-fir (Pseudotsuga menziesii) is native to western North America. It grows in a wide range of environmental conditions and is an important timber tree. Although there are several studies on the gene expression responses of Douglas-fir to abiotic cues, the absence of high-quality transcriptome and genome data is a barrier to further investigation. Like for most conifers, the available transcriptome and genome reference dataset for Douglas-fir remains fragmented and requires refinement. We aimed to generate a highly accurate, and complete reference transcriptome and genome annotation. We deep-sequenced the transcriptome of Douglas-fir needles from seedlings that were grown under nonstress control conditions or a combination of heat and drought stress conditions using long-read (LR) and short-read (SR) sequencing platforms. We used 2 computational approaches, namely de novo and genome-guided LR transcriptome assembly. Using the LR de novo assembly, we identified 1.3X more high-quality transcripts, 1.85X more "complete" genes, and 2.7X more functionally annotated genes compared to the genome-guided assembly approach. We predicted 666 long noncoding RNAs and 12,778 unique protein-coding transcripts including 2,016 putative transcription factors. We leveraged the LR de novo assembled transcriptome with paired-end SR and a published single-end SR transcriptome to generate an improved genome annotation. This was conducted with BRAKER2 and refined based on functional annotation, repetitive content, and transcriptome alignment. This high-quality genome annotation has 51,419 unique gene models derived from 322,631 initial predictions. Overall, our informatics approach provides a new reference Douglas-fir transcriptome assembly and genome annotation with considerably improved completeness and functional annotation.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".