Dye shift: a neglected source of genotyping error in molecular ecology
Bibliographic record
Abstract
Molecular ecologists must be vigilant in detecting and accounting for genotyping error, yet potential errors stemming from dye-induced mobility shift (dye shift) may be frequently neglected and largely unknown to researchers who employ 3-primer systems with automated genotyping. When left uncorrected, dye shift can lead to mis-scoring alleles and even to falsely calling new alleles if different dyes are used to genotype the same locus in subsequent reactions. When we used four different fluorophore labels from a standard dye set to genotype the same set of loci, differences in the resulting size estimates for a single allele ranged from 2.07 bp to 3.68 bp. The strongest effects were associated with the fluorophore PET, and relative degree of dye shift was inversely related to locus size. We found little evidence in the literature that dye shift is regularly accounted for in 3-primer studies, despite knowledge of this phenomenon existing for over a decade. However, we did find some references to erroneous standard correction factors for the same set of dyes that we tested. We thus reiterate the need for strict quality control when attempting to reduce possible sources of genotyping error, and in cases where different dyes are applied to a single locus, perhaps mistakenly, we strongly discourage researchers from assuming generic correction patterns.
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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 | 0.036 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".