Accurate interpretation of genomic variants that alter RNA splicing is critical to precision
medicine. We present a computational framework, Prediction of variant Effect on Percent …

Improving predictions of phenotypic consequences for genomic variants is part of ongoing
efforts in the scientific community to gain meaningful insights into genomic function. Within …

Background Despite numerous molecular and computational advances, roughly half of
patients with a rare disease remain undiagnosed after exome or genome sequencing. A …

Background Variants that disrupt mRNA splicing account for a sizable fraction of the
pathogenic burden in many genetic disorders, but identifying splice-disruptive variants …

Background It is estimated that up to 50% of all disease causing variants disrupt splicing.
Due to its complexity, our ability to predict which variants disrupt splicing is limited, meaning …

Modeling splicing is essential for tackling the challenge of variant interpretation as each
nucleotide variation can be pathogenic by affecting pre‐mRNA splicing via …

Predicting the impact of coding and noncoding variants on splicing is challenging,
particularly in non-canonical splice sites, leading to missed diagnoses in patients. Existing …

Background Splicing variants are a major class of pathogenic mutations, with their severity
equivalent to nonsense mutations. However, redundant and degenerate splicing signals …

Pathogenic genetic variants often primarily affect splicing. However, it remains difficult to
quantitatively predict whether and how genetic variants affect splicing. In 2018, the fifth …

Splicing is a highly regulated process that depends on numerous factors. It is particularly
challenging to quantitatively predict how a mutation will affect precursor messenger RNA …