SPiP: Splicing Prediction Pipeline, a machine learning tool for massive detection of exonic and intronic variant effects on mRNA splicing. Issue 12 (20th November 2022)

• Record Type: Journal Article
• Authors: Leman, Raphaël; Parfait, Béatrice; Vidaud, Dominique; Girodon, Emmanuelle; Pacot, Laurence; Le Gac, Gérald; Ka, Chandran; Ferec, Claude; Fichou, Yann; Quesnelle, Céline; Aucouturier, Camille; Muller, Etienne; Vaur, Dominique; Castera, Laurent; Boulouard, Flavie; Ricou, Agathe; Tubeuf, Hélène; Soukarieh, Omar; Gaildrat, Pascaline; Riant, Florence; Guillaud‐Bataille, Marine; Caputo, Sandrine M.; Caux‐Moncoutier, Virginie; Boutry‐Kryza, Nadia; Bonnet‐Dorion, Françoise; Schultz, Ines; Rossing, Maria; Quenez, Olivier; Goldenberg, Louis; Harter, Valentin; Parsons, Michael T.; Spurdle, Amanda B.; Frébourg, Thierry; Martins, Alexandra; Houdayer, Claude; Krieger, Sophie;
• Is Part Of: Human mutation. Volume 43:Issue 12(2022)
• Page Start: 2308
• Page End: 2323
• Publication Date: 2022-11-20
• Subjects: machine learning -- RNA -- sequence variants -- SPiP -- splicing predictions; Human chromosome abnormalities -- Periodicals; Mutation (Biology) -- Periodicals; 616.04205
• DOI: 10.1002/humu.24491
• Languages: English
• ISSNs: 1059-7794

Abstract

Abstract: 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 a machine learning approach, a comprehensive assessment of the variant effect on different splicing motifs. We gathered a curated set of 4616 variants scattered all along the sequence of 227 genes, with their corresponding splicing studies. The Bayesian analysis provided us with the number of control variants, that is, 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, better than SpliceAI and SQUIRLS (0.965 and 0.766) for the same data set. 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/