Additional file 2 of Rare deleterious mutations of HNRNP genes result in shared neurodevelopmental disorders
Bibliographic record
Abstract
Additional file 2: Supplementary Figures S1-S5. Fig. S1. Dendrogram of hnRNPs based on multiple sequence alignment of canonical amino acid sequences. Colors match those seen in Fig. 2. NDD hnRNPs are shown in black boxes. Fig. S2. Pathogenicity assessment of variation in hnRNPs. A) Gene Variation Intolerance Ranking (GeVIR), loss-of-function observed/expected upper bound fraction (LOEUF), and Variation Intolerant Region Loss-of-Function (VIRLoF) percentiles. Average LOEUF percentile is significantly higher for NDD HNRNPs (n = 13) compared to other HNRNPs (n = 15). B) Average fold change for GeVIR, LEOUF, and VIRLoF for autosomal dominant (AD) and autosomal recessive (AR) variants. Average LEOUF fold change for AD mutations is significantly higher for NDD HNRNPs compared to other HNRNPs, with the AD VIRLoF fold change trending in the same direction. The AR LEOUF fold change is trending towards being significantly higher among other HNRNPs compared to NDD HNRNPs. One-way t-test. * p < 0.05. Fig. S3. Expression of HNRNPs among adult tissues and the developing human cortex. A) Heatmap of all HNRNP expression in developing cortex tissues. B) Comparison of fold expression of NDD HNRNPs to non-NDD HNRNPs. C) Heatmap of all HNRNP expression (transcript level expression) in adult brain tissues from GTEx. D) Heatmap of NDD HNRNP expression (transcript level expression) in all tissues from GTEx. Fig. S4. Phenotypic correlations for LGD and missense variant probands. A) Correlation matrix of phenotypes across HNRNP probands with LGD variation (genes with only missense variation excluded) and B) severe missense variation (genes with only LGD variation excluded. * p < 0.05; ** p < 0.01; *** p < 0.001; **** p < 0.0001. Fig. S5. Phenotypic comparisons between LGD and missense variants by HNRNP. Fig. S6. GTEx expression of NDD HNRNPs and genes associated with similarly presenting disorders.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.888 | 0.176 |
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".