A transcriptomic approach to identify genes associated with wood density in<i>Picea sitchensis</i>
Bibliographic record
Abstract
Patrick G. Stephensona, Nicole Harrisa, Joan E. Cottrellb, Steven G. Ralphc, Jöerg Bohlmannd & Gail Taylora* a School of Biological Sciences , University of Southampton , Southampton, UK b Forest Research , Northern Research Station, Roslin , Midlothian, Scotland c Department of Biology , University of North Dakota , Grand Forks, ND, USA d Michael Smith Laboratories , University of British Columbia , Vancouver, BC, Canada * School of Biological Sciences, Bassett Crescent East, University of Southampton, Southampton, SO16 7PX, UK E-mail: g.taylor@soton.ac.uk Abstract The demand for trees for industrial application is growing steadily and not expected to plateau for at least two decades; consequently, the supply of wood must increase to meet this need. One method for increasing yield without compromising land requirements is to modify wood density. The current study uses a transcriptomic approach to identify the genes associated with wood density that are likely to be of value in Sitka spruce (Picea sitchensis (Bong.) Carr.) breeding programmes. Following extensive wood density analysis from a Sitka field grown clonal trial, three detailed microarray studies were conducted to compare the transcriptome of cambium and xylem tissue from contrasting density clonal lines. Twenty-five genes exhibited differential expression, with up to 50-fold differences in expression observed, in at least two of the three microarray experiments, and this was verified using real-time polymerase chain reaction. Identified genes included those involved in cell wall synthesis, transcriptional regulation and plant pathogen defence functional categories. A wide range of processes influence wood density, but this study has identified potential regulators in these pathways. Future studies can now use this information to understand natural variation in wood density, and manipulate the expression of these genes to improve timber quality and yield.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".