We have developed a novel machine-learning approach, MutPred Splice, for the
identification of coding region substitutions that disrupt pre-mRNA splicing. Applying …

In silico tools have been developed to predict variants that may have an impact on pre-
mRNA splicing. The major limitation of the application of these tools to basic research and …

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

Background Single point mutations at both synonymous and non-synonymous positions
within exons can have severe effects on gene function through disruption of splicing …

Transcriptome complexity and its relation to numerous diseases underpins the need to
predict in silico splice variants and the regulatory elements that affect them. Building upon …

It is estimated that between 35% and 74% of all human genes can undergo alternative
splicing. Currently, the most efficient methods for large-scale detection of alternative splicing …

Sequence variants that may result in splicing alterations are a particular class of inherited
variants for which consequences can be more readily assessed, using a combination of …

Recent progress in deep learning has greatly improved the prediction of RNA splicing from
DNA sequence. Here, we present Pangolin, a deep learning model to predict splice site …

For more than three decades, researchers have known that consensus splice sites alone are
not sufficient regulatory elements to provide complex splicing regulation. Other regulators …

In silico tools for splicing defect prediction have a key role to assess the impact of variants of
uncertain significance. Our aim was to evaluate the performance of a set of commonly used …