Properties of Samples With Segregating Polymerase Chain Reaction (PCR) Dropout Mutations Within a Species
Bibliographic record
Abstract
In polymerase chain reaction (PCR)-based DNA sequencing studies, there is the possibility that mutations at the binding sites of primers result in no primer binding and therefore no amplification. In this article, we call such mutations PCR dropouts and present a coalescent-based theory of the distribution of segregating PCR dropout mutations within a species. We show that dropout mutations typically occur along branch sections that are at or near the base of a coalescent tree, if at all. Given that a dropout mutation occurs along a branch section near the base of a tree, there is a good chance that it causes the alleles of a large fraction of a species to go unamplified, which distorts the tree shape. Expected coalescence times and distributions of pairwise sequence differences in the presence of PCR dropout mutations are derived under the assumptions of both neutrality and background selection. These expectations differ from when PCR dropout mutations are absent and may form the basis of inferential approaches to detect the presence of dropout mutations, as well as the development of unbiased estimators of statistics associated with population-level genetic variation.
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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 | 0.009 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".