The Efficacy of Whole Genome Sequencing and RNA-Seq in the Diagnosis of Whole Exome Sequencing Negative Patients with Complex Neurological Phenotypes
Bibliographic record
Abstract
Whole-genome sequencing (WGS) is being increasingly utilized for the diagnosis of neurological disease by sequencing both the exome and the remaining 98 to 99% of the genetic code. In addition to more complete coverage, WGS can detect structural variants (SVs) and intronic variants (SNVs) that cannot be identified by whole exome sequencing (WES) or chromosome microarray (CMA). Other multi-omics tools, such as RNA sequencing (RNA-Seq), can be used in conjunction with WGS to functionally validate certain variants by detecting changes in gene expression and splicing. The objective of this retrospective study was to measure the diagnostic yield of duo/trio-based WGS and RNA-Seq in a cohort of 22 patients (20 families) with pediatric onset neurological phenotypes and negative or inconclusive WES results in lieu of reanalysis. WGS with RNA-Seq resulted in a definite diagnosis of an additional 25% of cases. Sixty percent of these solved cases arose from the identification of variants that were missed by WES. Variants that could not be unequivocally proven to be causative of the patients' condition were identified in an additional 5% of cases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".