Erratum to: Mutation screening in 86 known X-linked mental retardation genes by droplet-based multiplex PCR and massive parallel sequencing
Bibliographic record
Abstract
UNLABELLED: Massive parallel sequencing has revolutionized the search for pathogenic variants in the human genome, but for routine diagnosis, re-sequencing of the complete human genome in a large cohort of patients is still far too expensive. Recently, novel genome partitioning methods have been developed that allow to target re-sequencing to specific genomic compartments, but practical experience with these methods is still limited. In this study, we have combined a novel droplet-based multiplex PCR method and next generation sequencing to screen patients with X-linked mental retardation (XLMR) for mutations in 86 previously identified XLMR genes. In total, affected males from 24 large XLMR families were analyzed, including three in whom the mutations were already known. Amplicons corresponding to functionally relevant regions of these genes were sequenced on an Illumina/Solexa Genome Analyzer II platform. Highly specific and uniform enrichment was achieved: on average, 67.9% unambiguously mapped reads were derived from amplicons, and for 88.5% of the targeted bases, the sequencing depth was sufficient to reliably detect variations. Potentially disease-causing sequence variants were identified in 10 out of 24 patients, including the three mutations that were already known, and all of these could be confirmed by Sanger sequencing. The robust performance of this approach demonstrates the general utility of droplet-based multiplex PCR for parallel mutation screening in hundreds of genes, which is a prerequisite for the diagnosis of mental retardation and other disorders that may be due to defects of a wide variety of genes. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11568-010-9137-y) contains supplementary material, which is available to authorized users.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.011 |
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".