Targeted enrichment beyond the consensus coding DNA sequence exome reveals exons with higher variant densities
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: Enrichment of loci by DNA hybridization-capture, followed by high-throughput sequencing, is an important tool in modern genetics. Currently, the most common targets for enrichment are the protein coding exons represented by the consensus coding DNA sequence (CCDS). The CCDS, however, excludes many actual or computationally predicted coding exons present in other databases, such as RefSeq and Vega, and non-coding functional elements such as untranslated and regulatory regions. The number of variants per base pair (variant density) and our ability to interrogate regions outside of the CCDS regions is consequently less well understood. RESULTS: We examine capture sequence data from outside of the CCDS regions and find that extremes of GC content that are present in different subregions of the genome can reduce the local capture sequence coverage to less than 50% relative to the CCDS. This effect is due to biases inherent in both the Illumina and SOLiD sequencing platforms that are exacerbated by the capture process. Interestingly, for two subregion types, microRNA and predicted exons, the capture process yields higher than expected coverage when compared to whole genome sequencing. Lastly, we examine the variation present in non-CCDS regions and find that predicted exons, as well as exonic regions specific to RefSeq and Vega, show much higher variant densities than the CCDS. CONCLUSIONS: We show that regions outside of the CCDS perform less efficiently in capture sequence experiments. Further, we show that the variant density in computationally predicted exons is more than 2.5-times higher than that observed in the CCDS.
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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