Comparison of DNA- and RNA-Based Methods for Detection of TruncatingBRCA1 Mutations
Why this work is in the frame
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Bibliographic record
Abstract
A number of methods are used for mutational analysis of BRCA1, a large multi-exon gene. A comparison was made of five methods to detect mutations generating premature stop codons that are predicted to result in synthesis of a truncated protein in BRCA1. These included four DNA-based methods: two-dimensional gene scanning (TDGS), denaturing high performance liquid chromatography (DHPLC), enzymatic mutation detection (EMD), and single strand conformation polymorphism analysis (SSCP) and an RNA/DNA-based protein truncation test (PTT) with and without complementary 5' sequencing. DNA and RNA samples isolated from 21 coded lymphoblastoid cell line samples were tested. These specimens had previously been analyzed by direct automated DNA sequencing, considered to be the optimum method for mutation detection. The set of 21 cell lines included 14 samples with 13 unique frameshift or nonsense mutations, three samples with two unique splice site mutations, and four samples without deleterious mutations. The present study focused on the detection of protein-truncating mutations, those that have been reported most often to be disease-causing alterations that segregate with cancer in families. PTT with complementary 5' sequencing correctly identified all 15 deleterious mutations. Not surprisingly, the DNA-based techniques did not detect a deletion of exon 22. EMD and DHPLC identified all of the mutations with the exception of the exon 22 deletion. Two mutations were initially missed by TDGS, but could be detected after slight changes in the test design, and five truncating mutations were missed by SSCP. It will continue to be important to use complementary methods for mutational analysis.
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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