An improved molecular inversion probe based targeted sequencing approach for low variant allele frequency
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Deep targeted sequencing technologies are still not widely used in clinical practice due to the complexity of the methods and their cost. The Molecular Inversion Probes (MIP) technology is cost effective and scalable in the number of targets, however, suffers from low overall performance especially in GC rich regions. In order to improve the MIP performance, we sequenced a large cohort of healthy individuals (n = 4417), with a panel of 616 MIPs, at high depth in duplicates. To improve the previous state-of-the-art statistical model for low variant allele frequency, we selected 4635 potentially positive variants and validated them using amplicon sequencing. Using machine learning prediction tools, we significantly improved precision of 10–56.25% (P < 0.0004) to detect variants with VAF > 0.005. We further developed biochemically modified MIP protocol and improved its turn-around-time to ∼4 h. Our new biochemistry significantly improved uniformity, GC-Rich regions coverage, and enabled 95% on target reads in a large MIP panel of 8349 genomic targets. Overall, we demonstrate an enhancement of the MIP targeted sequencing approach in both detection of low frequency variants and in other key parameters, paving its way to become an ultrafast cost-effective research and clinical diagnostic tool.
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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