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 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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".