A Simple Variable Number of Tandem Repeat-Based Genotyping Strategy for the Detection of Handling Errors and Validation of Sample Identity in Biobanks
Bibliographic record
Abstract
Biobanking biological samples involve multiple handling, processing, and labeling steps. Each step may be a source of error, which if unnoticed or uncorrected may have consequences for research. We aimed to develop a simple and inexpensive genotyping method that would be valuable to detect such errors and confirm sample identity. For this purpose, seven variable number of tandem repeat (VNTR) loci were selected, analyzed by polymerase chain reaction (PCR) amplification, and organized in a PCR-based DNA profiling algorithm that proved useful to minimize the number of steps required for the procedure. Match probability calculations suggest that this method/algorithm has the potential to discriminate every participant of a biobank. As a proof of concept, the algorithm was applied on samples taken from the PROCURE Prostate Cancer Biobank. It was applied on 403 DNA samples from 101 randomly chosen patients who provided prostate tissues at surgery and blood at two to three different time points over a period of up to 7 years. A unique DNA profile requiring the analysis of no more than four VNTR loci (D16S83, D17S5, D1S80, D19S20) was successfully obtained for each of the 101 cases studied and led to the identification of two mismatches among the 403 samples evaluated (0.5% error rate). Further investigations using the same genotyping method revealed that one of the errors was due to tissue mishandling and that the other was due to tissue mislabeling. These errors, typical to the complex biobanking process, highlight the importance to implement a routine genotyping method as part of quality assurance in biobanking.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".