Exome capture from the spruce and pine giga‐genomes
Bibliographic record
Abstract
Sequence capture is a flexible tool for generating reduced representation libraries, particularly in species with massive genomes. We used an exome capture approach to sequence the gene space of two of the dominant species in Canadian boreal and montane forests - interior spruce (Picea glauca x engelmanii) and lodgepole pine (Pinus contorta). Transcriptome data generated with RNA-seq were coupled with draft genome sequences to design baits corresponding to 26 824 genes from pine and 28 649 genes from spruce. A total of 579 samples for spruce and 631 samples for pine were included, as well as two pine congeners and six spruce congeners. More than 50% of targeted regions were sequenced at >10× depth in each species, while ~12% captured near-target regions within 500 bp of a bait position were sequenced to a depth >10×. Much of our read data arose from off-target regions, which was likely due to the fragmented and incomplete nature of the draft genome assemblies. Capture in general was successful for the related species, suggesting that baits designed for a single species are likely to successfully capture sequences from congeners. From these data, we called approximately 10 million SNPs and INDELs in each species from coding regions, introns, untranslated and flanking regions, as well as from the intergenic space. Our study demonstrates the utility of sequence capture for resequencing in complex conifer genomes, suggests guidelines for improving capture efficiency and provides a rich resource of genetic variants for studies of selection and local adaptation in these species.
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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) | 0.000 | 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 | 0.001 |
| Research integrity | 0.001 | 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".