A next-generation sequencing method for gene doping detection that distinguishes low levels of plasmid DNA against a background of genomic DNA
Bibliographic record
Abstract
Gene doping confers health risks for athletes and is a threat to fair competition in sports. Therefore the anti-doping community has given attention on its detection. Previously published polymerase chain reaction-based methodologies for gene doping detection are targeting exon-exon junctions in the intron-less transgene. However, because these junctions are known, it would be relatively easy to evade detection by tampering with the copyDNA sequences. We have developed a targeted next-generation sequencing based assay for the detection of all exon-exon junctions of the potential doping genes, EPO, IGF1, IGF2, GH1, and GH2, which is resistant to tampering. Using this assay, all exon-exon junctions of copyDNA of doping genes could be detected with a sensitivity of 1296 copyDNA copies in 1000 ng of genomic DNA. In addition, promotor regions and plasmid-derived sequences are readily detectable in our sequence data. While we show the reliability of our method for a selection of genes, expanding the panel to detect other genes would be straightforward. As we were able to detect plasmid-derived sequences, we expect that genes with manipulated junctions, promotor regions, and plasmid or virus-derived sequences will also be readily detected.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".