Assessing In Silico Tools for Accurate Pathogenicity Prediction in CHD Nucleosome Remodelers
Bibliographic record
Abstract
Chromodomain Helicase DNA-binding (CHD) proteins compose a family of chromatin remodelers that play crucial roles in DNA repair, gene expression regulation, neural stem cell differentiation and chromatin integrity. Genetic variants in CHD chromatin remodelers are associated with neurodevelopmental disorders with features like autism spectrum disorder and intellectual disability. Consequently, the determination of variant pathogenicity in clinical genetic tests for individuals bearing CHD variants is crucial. In this study, we compared the efficiency of multiple pathogenicity prediction tools, which are valuable resources for the identification and annotation of potentially disease-causing variants, to assess the most accurate in silico tool capable of distinguishing pathogenic CHD variants from benign ones. We have focused specifically on genes that share high structural and functional similarity and are strongly linked to pathogenic mutations. Here, we evaluated a range of pathogenicity prediction tools and compared their output with pathogenicity conclusions reported in the literature and genomic databases. Our findings showed that the top performing tools were BayesDel, ClinPred, AlphaMissense, ESM-1b and SIFT. BayesDel, specifically with its addAF component, was overall the most robust tool for CHD variant pathogenicity prediction. We also suggest incorporating SnpEff's high-impact variant identification capabilities for the development of a hybrid tool that would enhance the classification of CHD variants. Our study emphasizes the need for continuous evaluation and integration of updated prediction tools, including emerging artificial intelligence (AI) approaches. This research also emphasizes the importance of gathering better clinical and mechanistic data on the deleteriousness of pathogenic variants to improve the accuracy of clinical diagnostics.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".