A Fluorescent Multiplex-DGGE Screening Test for Mutations in the BRCA1 Gene
Bibliographic record
Abstract
Screening for mutations in the BRCA1 gene is challenging because of the wide spectrum of mutations found in this large gene. As the extensive exon 11 is commonly screened by the protein truncation test (PTT), here a fluorescent multiplex denaturing gradient gel electrophoresis (FMD) mutation screening technique was developed to test the remaining numerous small exons and splice sites of the gene. The method is based upon the use of an efficient multiplex polymerase chain reaction (PCR) amplification of the target regions, followed by denaturing gradient gel electrophoresis (DGGE) separation of the amplicon mixture, and the immediate achievement of results by wet gel scanning. The technique was applied to screen 16 samples with different BRCA1 sequence variants distributed over 12 exons. All variants were detected. In addition, 188 DNA samples from ovarian cancer patients were screened, identifying 22 new sequence variants (11.7% of the samples) and 243 common polymorphisms in the BRCA1 locus. Variants included 16 single nucleotide substitutions, 3 deletions of 2 nucleotides, 1 deletion of 4 nucleotides, and 2 insertions of 1 nucleotide. The FMD test provides an accurate, fast, nonradioactive and cost-efficient way to scan the BRCA1 gene with high sensitivity and an ease of result interpretation. This technique may prove to be a useful research tool for the detection of mutations and polymorphisms in the BRCA1 gene and for large-scale epidemiologic studies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".