Development of a fast PCR protocol enabling rapid generation of AmpFlSTR(R) Identifiler(R)profiles for genotyping of human DNA
Bibliographic record
Abstract
BACKGROUND: Traditional PCR methods for forensic STR genotyping require approximately 2.5 to 4 hours to complete, contributing a significant portion of the time required to process forensic DNA samples. The purpose of this study was to develop and validate a fast PCR protocol that enabled amplification of the 16 loci targeted by the AmpFℓSTR® Identifiler® primer set, allowing decreased cycling times. METHODS: Fast PCR conditions were achieved by substituting the traditional Taq polymerase for SpeedSTAR™ HS DNA polymerase which is designed for fast PCR, by upgrading to a thermal cycler with faster temperature ramping rates and by modifying cycling parameters (less time at each temperature) and adopting a two-step PCR approach. RESULTS: The total time required for the optimized protocol is 26 min. A total of 147 forensically relevant DNA samples were amplified using the fast PCR protocol for Identifiler. Heterozygote peak height ratios were not affected by fast PCR conditions, and full profiles were generated for single-source DNA amounts between 0.125 ng and 2.0 ng. Individual loci in profiles produced with the fast PCR protocol exhibited average n-4 stutter percentages ranging from 2.5 ± 0.9% (THO1) to 9.9 ± 2.7% (D2S1338). No increase in non-adenylation or other amplification artefacts was observed. Minor contributor alleles in two-person DNA mixtures were reliably discerned. Low level cross-reactivity (monomorphic peaks) was observed with some domestic animal DNA. CONCLUSIONS: The fast PCR protocol presented offers a feasible alternative to current amplification methods and could aid in reducing the overall time in STR profile production or could be incorporated into a fast STR genotyping procedure for time-sensitive situations.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| 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) | 0.004 | 0.005 |
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".