Protocol: A high-throughput DNA extraction system suitable for conifers
Bibliographic record
Abstract
BACKGROUND: High throughput DNA isolation from plants is a major bottleneck for most studies requiring large sample sizes. A variety of protocols have been developed for DNA isolation from plants. However, many species, including conifers, have high contents of secondary metabolites that interfere with the extraction process or the subsequent analysis steps. Here, we describe a procedure for high-throughput DNA isolation from conifers. RESULTS: We have developed a high-throughput DNA extraction protocol for conifers using an automated liquid handler and modifying the Qiagen MagAttract Plant Kit protocol. The modifications involve change to the buffer system and improving the protocol so that it almost doubles the number of samples processed per kit, which significantly reduces the overall costs. We describe two versions of the protocol: one for medium-throughput (MTP) and another for high-throughput (HTP) DNA isolation. The HTP version works from start to end in the industry-standard 96-well format, while the MTP version provides higher DNA yields per sample processed. We have successfully used the protocol for DNA extraction and genotyping of thousands of individuals of several spruce and a pine species. CONCLUSION: A high-throughput system for DNA extraction from conifer needles and seeds has been developed and validated. The quality of the isolated DNA was comparable with that obtained from two commonly used methods: the silica-spin column and the classic CTAB protocol. Our protocol provides a fully automatable and cost effective solution for processing large numbers of conifer samples.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.026 | 0.039 |
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".