Journal
HUMAN MUTATION
Volume 43, Issue 12, Pages 2308-2323Publisher
WILEY-HINDAWI
DOI: 10.1002/humu.24491
Keywords
machine learning; RNA; sequence variants; SPiP; splicing predictions
Categories
Funding
- French Fondation de France [200412859]
- Institut National du Cancer/Direction Generale de l'Offre de Soins
- Groupement des Entreprises Francaises dans la Lutte contre le Cancer (Gefluc) [R18064EE]
- CIFRE PhD fellowship
- French Association Nationale de la Recherche et de la Technologie (ANRT) [2015/0335]
- NHMRC Senior Research Fellowship [ID1061779]
- Cancer Council Queensland [ID1086286]
- Canceropple Nord-Ouest (CNO)
- Institut National du Cancer (INCa)
- OpenHealth Institute
- Federation Hospitalo-Universitaire (FHU)
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This article introduces a splicing prediction pipeline called SPiP, which comprehensively assesses the impact of variants on different splicing motifs using a machine learning approach. The results show that SPiP has high sensitivity and specificity, and can effectively detect spliceogenic variants.
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:
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