Properties of sequence-tagged-site primer sets influencing repeatability
Bibliographic record
Abstract
The polymerase chain reaction (PCR) has become a standard procedure in plant genetics, and is the basis for many emerging genomics approaches to mapping and gene identification. One advantage of PCR is that sequence information for primer sets can be exchanged between laboratories, obviating the need for exchange and maintenance of biological materials. Repeatability of primer sets, whereby the same products are amplified in different laboratories using the same primer set, is important to successful exchange and utilization. We have developed several hundred sequence-tagged site (STS) primer sets for wheat and barley. The ability of the primer sets to generate reproducible amplifications in other laboratories has been variable. We wished to empirically determine the properties of the primer sets that most influenced repeatability. A total of 96 primer sets were tested with four genomic DNA samples on each of four thermocyclers. All major bands were repeatable across all four thermocylers for approximately 50% of the primer sets. Characteristics most often associated with differences in repeatability included primer GC content and 3'-end stability of the primers. The propensity for primer-dimer formation was not a factor in repeatability. Our results provide empirical direction for the development of repeatable primer sets.
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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 | 0.021 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".