Show Notes
️ Episode 3: Heuristics in Splicing: Rethinking Variant Impact from the Genome Up
In this episode of Base by Base, we discuss a 2025 study published in The American Journal of Human Genetics that introduces a comprehensive, data-driven framework to enhance the assessment of splice-altering variants (SAVs). These variants, often overlooked in variant classification workflows, are now being reevaluated with a new lens grounded in splicing biology and empirical data.
The study led by Sullivan and colleagues proposes a heuristic-based system built on the analysis of over 200,000 annotated exons, 19,000 branchpoints, and nearly 12,000 experimentally validated SAVs from the SpliceVarDB. Their goal: to provide interpretable, biologically-informed rules to bridge the gap between black-box in silico predictors and the true functional consequences of variants on splicing.
Key insights include:
Introduction of a splicing checklist based on motif strength, spacing, and sequence constraints to assess cryptic or disrupted splice sites.
Heuristic classification schemes for donor and acceptor site disruptions, refined with empirical spliceogenicity metrics.
Recognition of pseudoexon creation mechanisms, showing how deep intronic variants can activate latent splice sites.
Emphasis on context-driven outcomes and variant location for interpreting likely splicing alterations, including exon skipping, truncation, and intron retention.
This episode highlights how curated heuristics, rooted in biological principles, can enhance variant interpretation beyond existing AI tools, ultimately improving the clinical assessment of pathogenicity.
Reference: Sullivan, P.J., Quinn, J.M.W., Ajuyah, P., et al. (2025). Data-driven insights to inform splice-altering variant assessment. The American Journal of Human Genetics, 112(5), 764–778. https://doi.org/10.1016/j.ajhg.2025.02.012
License: This content is distributed under the Creative Commons Attribution 4.0 International License (CC BY 4.0). For more information, visit https://creativecommons.org/licenses/by/4.0/
