Hi-Plex for high-throughput mutation screening: application to the breast cancer susceptibility gene PALB2
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Massively parallel sequencing (MPS) has revolutionised biomedical research and offers enormous capacity for clinical application. We previously reported Hi-Plex, a streamlined highly-multiplexed PCR-MPS approach, allowing a given library to be sequenced with both the Ion Torrent and TruSeq chemistries. Comparable sequencing efficiency was achieved using material derived from lymphoblastoid cell lines and formalin-fixed paraffin-embedded tumour. METHODS: Here, we report high-throughput application of Hi-Plex by performing blinded mutation screening of the coding regions of the breast cancer susceptibility gene PALB2 on a set of 95 blood-derived DNA samples that had previously been screened using Sanger sequencing and high-resolution melting curve analysis (n = 90), or genotyped by Taqman probe-based assays (n = 5). Hi-Plex libraries were prepared simultaneously using relatively inexpensive, readily available reagents in a simple half-day protocol followed by MPS on a single MiSeq run. RESULTS: We observed that 99.93% of amplicons were represented at ≥10X coverage. All 56 previously identified variant calls were detected and no false positive calls were assigned. Four additional variant calls were made and confirmed upon re-analysis of previous data or subsequent Sanger sequencing. CONCLUSIONS: These results support Hi-Plex as a powerful approach for rapid, cost-effective and accurate high-throughput mutation screening. They further demonstrate that Hi-Plex methods are suitable for and can meet the demands of high-throughput genetic testing in research and clinical settings.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it