Hi-Plex for high-throughput mutation screening: application to the breast cancer susceptibility gene PALB2
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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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".