Novel diagnostic tool for prediction of variant spliceogenicity derived from a set of 395 combined in silico/in vitro studies: an international collaborative effort. Issue 15 (10th May 2018)

• Record Type: Journal Article
• Authors: Leman, Raphaël; Gaildrat, Pascaline; Le Gac, Gérald; Ka, Chandran; Fichou, Yann; Audrezet, Marie-Pierre; Caux-Moncoutier, Virginie; Caputo, Sandrine M; Boutry-Kryza, Nadia; Léone, Mélanie; Mazoyer, Sylvie; Bonnet-Dorion, Françoise; Sevenet, Nicolas; Guillaud-Bataille, Marine; Rouleau, Etienne; Bressac-de Paillerets, Brigitte; Wappenschmidt, Barbara; Rossing, Maria; Muller, Danielle; Bourdon, Violaine; Revillon, Françoise; Parsons, Michael T; Rousselin, Antoine; Davy, Grégoire; Castelain, Gaia; Castéra, Laurent; Sokolowska, Joanna; Coulet, Florence; Delnatte, Capucine; Férec, Claude; Spurdle, Amanda B; Martins, Alexandra; Krieger, Sophie; Houdayer, Claude;
• Is Part Of: Nucleic acids research. Volume 46:Issue 15(2018)
• Page Start: 7913
• Page End: 7923
• Publication Date: 2018-05-10
• Subjects: Nucleic acids -- Periodicals; Molecular biology -- Periodicals; 572.805
• DOI: 10.1093/nar/gky372
• Languages: English
• ISSNs: 0305-1048

Abstract

Abstract: Variant interpretation is the key issue in molecular diagnosis. Spliceogenic variants exemplify this issue as each nucleotide variant can be deleterious via disruption or creation of splice site consensus sequences. Consequently, reliable in silico prediction of variant spliceogenicity would be a major improvement. Thanks to an international effort, a set of 395 variants studied at the mRNA level and occurring in 5′ and 3′ consensus regions (defined as the 11 and 14 bases surrounding the exon/intron junction, respectively) was collected for 11 different genes, including BRCA1, BRCA2, CFTR and RHD, and used to train and validate a new prediction protocol named Splicing Prediction in Consensus Elements (SPiCE). SPiCE combines in silico predictions from SpliceSiteFinder-like and MaxEntScan and uses logistic regression to define optimal decision thresholds. It revealed an unprecedented sensitivity and specificity of 99.5 and 95.2%, respectively, and the impact on splicing was correctly predicted for 98.8% of variants. We therefore propose SPiCE as the new tool for predicting variant spliceogenicity. It could be easily implemented in any diagnostic laboratory as a routine decision making tool to help geneticists to face the deluge of variants in the next-generation sequencing era. SPiCE is accessible at (https://sourceforge.net/projects/spicev2-1/ ).