Sequence Capture and Next-Generation Resequencing of Multiple Tagged Nucleic Acid Samples for Mutation Screening of Urea Cycle Disorders
Bibliographic record
Abstract
BACKGROUND: Molecular genetic testing is commonly used to confirm clinical diagnoses of inherited urea cycle disorders (UCDs); however, conventional mutation screenings encompassing only the coding regions of genes may not detect disease-causing mutations occurring in regulatory elements and introns. Microarray-based target enrichment and next-generation sequencing now allow more-comprehensive genetic screening. We applied this approach to UCDs and combined it with the use of DNA bar codes for more cost-effective, parallel analyses of multiple samples. METHODS: We used sectored 2240-feature medium-density oligonucleotide arrays to capture and enrich a 199-kb genomic target encompassing the complete genomic regions of 3 urea cycle genes, OTC (ornithine carbamoyltransferase), CPS1 (carbamoyl-phosphate synthetase 1, mitochondrial), and NAGS (N-acetylglutamate synthase). We used the Genome Sequencer FLX System (454 Life Sciences) to jointly analyze 4 samples individually tagged with a 6-bp DNA bar code and compared the results with those for an individually sequenced sample. RESULTS: Using a low tiling density of only 1 probe per 91 bp, we obtained strong enrichment of the targeted loci to achieve ≥90% coverage with up to 64% of the sequences covered at a sequencing depth ≥10-fold. We observed a very homogeneous sequence representation of the bar-coded samples, which yielded a >30% increase in the sequence data generated per sample, compared with an individually processed sample. Heterozygous and homozygous disease-associated mutations were correctly detected in all samples. CONCLUSIONS: The use of DNA bar codes and the use of sectored oligonucleotide arrays for target enrichment enable parallel, large-scale analysis of complete genomic regions for multiple genes of a disease pathway and for multiple samples simultaneously. This approach thus may provide an efficient tool for comprehensive diagnostic screening of mutations.
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How this classification was reachedexpand
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 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".