ArticleAmerican journal of human genetics2025
Data-driven insights to inform splice-altering variant assessment.
Article in American journal of human genetics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- GenART: Reading the genome's language with adaptive "words".Cell reports methods · 2026Article
- Targeted gene editing ofMolecular therapy. Nucleic acids · 2026Article
- The periphery of nuclear speckles defines a spatially and temporally regulated compartment of long-lived intron-retained RNAs that resolves during mitosis.Nature cell biology · 2026Article
- When splicing is not all or none: GT>GC 5' splice-site variants as a model for intermediate effects and challenges in variant classification.HGG advances · 2026Article
- HiFi long-read RNA sequencing enhances clinical diagnostics in rare disorders.European journal of human genetics : EJHG · 2026Article
- Pathogenicity prediction for noncanonical splice-altering variants based on multimodal feature fusion.Briefings in bioinformatics · 2026Article
- Using RNA-targeting CRISPR-Cas13 and engineered U1 systems to targetMolecular therapy. Nucleic acids · 2026Article
- Synonymous and non-synonymous variants at splice junctions can disrupt splicing and are frequently linked to disease associated loss of function genes.BMC genomics · 2025Article
- Investigation of GSDME results in the identification of the first pathogenic synonymous variants and genotype-phenotype correlations.Human genetics · 2025Article
- Unveiling splicing disruptions due to common somatic variants in acute myeloid leukemia.Human genomics · 2025Article
- Axonal polyneuropathy in Lafora disease with a newly identified EPM2A mutation: a case report and review of literature.Journal of neurology · 2025Article
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Authors and funding
6 authors.
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Abstract
Disease-causing genetic variants often disrupt mRNA splicing, an intricate process that is incompletely understood. Thus, accurate inference of which genetic variants will affect splicing and what their functional consequences will be is challenging, particularly for variants outside of the essential splice sites. Here, we describe a set of data-driven heuristics that inform the interpretation of human splice-altering variants (SAVs) based on the analysis of annotated exons, experimentally validated SAVs, and the currently understood principles of splicing biology. We defined requisite splicing criteria by examining around 202,000 canonical protein-coding exons and 19,000 experimentally validated splicing branchpoints. This analysis defined the sequence, spacing, and motif strength required for splicing, with 95.9% of the exons examined meeting these criteria. By considering over 12,000 experimentally validated variants from the SpliceVarDB, we defined a set of heuristics that inform the evaluation of putative SAVs. To ensure the applicability of each heuristic, only those supported by at least 10 experimentally validated variants were considered. This allowed us to establish a measure of spliceogenicity: the proportion of variants at a location (or motif site) that affected splicing in a given context. This study makes considerable advances toward bridging the gap between computational predictions and the biological process of splicing, offering an evidence-based approach to identifying SAVs and evaluating their impact. Our splicing heuristics enhance the current framework for genetic variant evaluation with a robust, detailed, and comprehensible analysis by adding valuable context over traditional binary prediction tools.
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