DMSO and betaine significantly enhance the PCR amplification of ITS2 DNA barcodes from plants
Bibliographic record
Abstract
ITS2 marker is highly efficient in species discrimination but its application in DNA barcoding is limited due to huge variations in the PCR success rate. We have hypothesized that higher GC content and the resultant secondary structures formed during annealing might hinder the PCR amplification of ITS2. To test this hypothesis, we selected 12 species from 12 different families in which ITS2 was not amplified under standard PCR reaction conditions. In these samples, DMSO, formamide, betaine, and 7-deaza-dGTP were evaluated for their ability to improve the PCR success rate. The highest PCR success rate (91.6%) was observed with 5% DMSO, followed by 1 M betaine (75%), 50 μM 7-deaza-dGTP (33.3%), and 3% formamide (16.6%). The one sample that did not amplify with DMSO was amplified by adding 1 M betaine. However, combining DMSO and betaine in the same reaction did not improve the PCR. Therefore, to achieve the highest PCR success rate for ITS2, it is recommended to include 5% DMSO by default and substitute it with 1 M betaine only in the case of failed reactions. When this strategy was tested in 50 species from 43 genera and 29 families, the PCR success rate of ITS2 increased from 42% to 100%.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".