Quantitative evaluation of bias in barcode markers derived from complex samples
Bibliographic record
Abstract
PCR products have become a major commodity used to identify organisms based on polymorphism at the DNA level. One problem arising is that unbiased identification of organisms takes as working hypothesis that when DNA is extracted from a sample, a positive signal will be obtained if universal primers are used and DNA quality is suitable for PCR. As this assumption is not always correct we used a system where large differences in PCR success have been described to identify where biases appear and maybe identify solutions. Plants can be identified with at least seven independent plastid‐located loci. These differ in their degree of PCR success and how informative they are in terms of taxonomically useful sequence polymorphisms. Here we used six common plastid loci spanning 48 plant species and performed a quantitative analysis of bias at each step of the identification process. As expected we found important differences in PCR efficiency within a single species, depending on the barcoding sequence being amplified. Quantitative PCR revealed that the Ct threshold for various plastid loci, even within a single species, could exhibit greater than 2000‐fold differences in DNA quantity after amplification. We then performed Next Generation Sequencing experiments in nine species using equal quantities of three plastid‐based primers and equally‐mixed quantities of DNA from multiple species. The result was significantly biased towards species and specific loci even when using adaptor‐specific primers. Our results caution that Next‐Generation Sequencing projects may suffer dramatic bias, arising largely during DNA amplification steps. Moreover, that amplification‐based Next Generation Sequencing technologies exhibit additional bias despite using adaptor‐specific primers, indicating that amplification success depends on the DNA fragment. As such, while qualitative analysis of unknown samples are prone to false negative results if a combination of widely‐successful amplicons are not used, quantitative results should be considered highly suspect, even if all species in the starting sample are known.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".