Modeling splicing is essential for tackling the challenge of variant interpretation as each nucleotide variation can be pathogenic by affecting pre-mRNA splicing via disruption/creation of splicing motifs such as 5’/3’ splice sites, branch sites or splicing regulatory elements. Unfortunately, most in silico tools focus on a specific type of splicing motif, which is why we developed the Splicing Prediction Pipeline (SPiP) to perform, in one single bioinformatic analysis based on machine learning approach, comprehensive assessment of variant effect on different splicing motifs. We gathered a curated set of 4,616 variants scattered all along the sequence of 227 genes, with their corresponding splicing studies. Bayesian analysis provided us the number of control variants, i.e. variants without impact on splicing, to mimic the deluge of variants from high throughput sequencing data. Results show that SPiP can deal with the diversity of splicing alterations, with 83.13% sensitivity and 99% specificity to detect spliceogenic variants. Overall performance as measured by area under the receiving operator curve was 0.986, significantly better than 0.965 spliceAI for the same dataset. SPiP lends itself to a unique suite for comprehensive prediction of spliceogenicity in the genomic medicine era. SPiP is available at: [https://sourceforge.net/projects/splicing-prediction-pipeline/](https://sourceforge.net/projects/splicing-prediction-pipeline/)

The large majority of germline alterations identified in the DNA mismatch repair (MMR) gene PMS2, a low-penetrance gene for the cancer predisposition Lynch Syndrome (LS, OMIM 120435), represent variants of unknown significance (VUS). The inability to assess pathogenicity of such VUS interferes with personalized healthcare. The complete in vitro MMR activity (CIMRA) assay, that only requires sequence information on the VUS, provides a functional analysis-based tool suited for VUS classification. To derive a formula that translates CIMRA assay results for PMS2 VUS into the odds of pathogenicity (OddsPath), we used a set of clinically classified PMS2 variants, supplemented by inactivating variants generated by an in cellulo genetic screen, as proxies for pathogenic variants. Validation of this OddsPath revealed very high predictive values for PMS2 VUS. We conclude that this OddsPath provides an integral metric that, similar to the other, higher penetrance, MMR proteins MSH2, MLH1 and MSH6, can be incorporated into the upcoming criteria for MMR gene VUS classification of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP). This will represent a seminal step forward in enabling personalized healthcare for individuals suspected of LS and their relatives.

It is possible to estimate the prior probability of pathogenicity for germline disease gene variants based on bioinformatic prediction of variant effect/s. However, routinely used approaches have likely led to the underestimation and underreporting of variants located outside donor and acceptor splice site motifs that affect mRNA processing. This review presents information about hereditary cancer gene germline variants, outside native splice sites, with experimentally validated splicing effects. We list 81 exonic variants that impact splicing regulatory elements in BRCA1, BRCA2, MLH1, MSH2, MSH6 and PMS2. We utilized a pre-existing large-scale BRCA1 functional dataset to map functional splicing regulatory elements, assess the relative performance of different tools to predict effects of 283 variants on such elements, and develop a generic workflow to prioritize variants that may impact splicing regulatory elements. We also describe rare examples of intronic variants that impact branchpoint sites and create pseudoexons. We discuss the challenges in predicting variant effect on branchpoint site usage and pseudoexonization, and suggest strategies to improve the bioinformatic prioritization of such variants for experimental validation. Importantly, our review highlights the importance of considering impact of variants outside donor and acceptor motifs on mRNA splicing and disease causation.