A catalogue of putative unique transcripts from Douglas-fir (Pseudotsuga menziesii) based on 454 transcriptome sequencing of genetically diverse, drought stressed seedlings
Bibliographic record
Abstract
BACKGROUND: Douglas-fir (Pseudotsuga menziesii) extends over a wide range of contrasting environmental conditions, reflecting substantial local adaptation. For this reason, it is an interesting model species to study plant adaptation and the effects of global climate change such as increased temperatures and significant periods of drought on individual trees and the forest landscape in general. However, genomic data and tools for studying genetic variation in natural populations to understand the genetic and physiological mechanisms of adaptation are currently missing for Douglas-fir. This study represents a first step towards characterizing the Douglas-fir transcriptome based on 454 sequencing of twelve cDNA libraries. The libraries were constructed from needle and wood tissue of coastal and interior provenances subjected to drought stress experiments. RESULTS: The 454 sequencing of twelve normalized cDNA libraries resulted in 3.6 million reads from which a set of 170,859 putative unique transcripts (PUTs) was assembled. Functional annotation by BLAST searches and Gene Ontology mapping showed that the composition of functional classes is very similar to other plant transcriptomes and demonstrated that a large fraction of the Douglas-fir transcriptome is tagged by the PUTs. Based on evolutionary conservation, we identified about 1,000 candidate genes related to drought stress. A total number of 187,653 single nucleotide polymorphisms (SNPs) were detected by three SNP detection tools. However, only 27,688 SNPs were identified by all three methods, indicating that SNP detection depends on the particular method used. The two alleles of about 60% of the 27,688 SNPs are segregating simultaneously in both coastal and interior provenances, which indicates a high proportion of ancestral shared polymorphisms or a high level of gene flow between these two ecologically and phenotypically different varieties. CONCLUSIONS: We established a catalogue of PUTs and large SNP database for Douglas-fir. Both will serve as a useful resource for the further characterization of the genome and transcriptome of Douglas-fir and for the analysis of genetic variation using genotyping or resequencing methods.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".