RegSNPs-intron: a computational framework for predicting pathogenic impact of intronic single nucleotide variants

Hai Lin; Katherine A Hargreaves; Rudong Li; Jill L Reiter; Yue Wang; Matthew Mort; David N Cooper; Yaoqi Zhou; Chi Zhang; Michael T Eadon; M Eileen Dolan; Joseph Ipe; Todd C Skaar; Yunlong Liu

doi:10.1186/s13059-019-1847-4

RegSNPs-intron: a computational framework for predicting pathogenic impact of intronic single nucleotide variants

Genome Biol. 2019 Nov 28;20(1):254. doi: 10.1186/s13059-019-1847-4.

Authors

Hai Lin^{1

2}, Katherine A Hargreaves³, Rudong Li^{1

2}, Jill L Reiter^{1

2}, Yue Wang², Matthew Mort⁴, David N Cooper⁴, Yaoqi Zhou⁵, Chi Zhang^{1

2}, Michael T Eadon⁶, M Eileen Dolan⁷, Joseph Ipe³, Todd C Skaar⁸, Yunlong Liu^{9

10}

Affiliations

¹ Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, 46202, USA.
² Department of Medical & Molecular Genetics, Indiana University School of Medicine, 410 West 10th Street, Suite 5000, Indianapolis, IN, 46202, USA.
³ Division of Clinical Pharmacology, Department of Medicine, Indiana University School of Medicine, 950 W Walnut St, Suite 419, Indianapolis, IN, 46202, USA.
⁴ Institute of Medical Genetics, Cardiff University, Heath Park, Cardiff, CF14 4XN, UK.
⁵ Institute for Glycomics and School of Informatics and Communication Technology, Griffith University, Parklands Dr., Southport, QLD, 4215, Australia.
⁶ Division of Nephrology, Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, 46202, USA.
⁷ Section of Hematology/Oncology, Department of Medicine, University of Chicago, Chicago, IL, 60637, USA.
⁸ Division of Clinical Pharmacology, Department of Medicine, Indiana University School of Medicine, 950 W Walnut St, Suite 419, Indianapolis, IN, 46202, USA. tskaar@iu.edu.
⁹ Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, 46202, USA. yunliu@iu.edu.
¹⁰ Department of Medical & Molecular Genetics, Indiana University School of Medicine, 410 West 10th Street, Suite 5000, Indianapolis, IN, 46202, USA. yunliu@iu.edu.

Abstract

Single nucleotide variants (SNVs) in intronic regions have yet to be systematically investigated for their disease-causing potential. Using known pathogenic and neutral intronic SNVs (iSNVs) as training data, we develop the RegSNPs-intron algorithm based on a random forest classifier that integrates RNA splicing, protein structure, and evolutionary conservation features. RegSNPs-intron showed excellent performance in evaluating the pathogenic impacts of iSNVs. Using a high-throughput functional reporter assay called ASSET-seq (ASsay for Splicing using ExonTrap and sequencing), we evaluate the impact of RegSNPs-intron predictions on splicing outcome. Together, RegSNPs-intron and ASSET-seq enable effective prioritization of iSNVs for disease pathogenesis.

Keywords: Bioinformatics; Computational biology; Disease pathogenesis; High-throughput screening assay; Intron; Prediction model; RNA splicing; Random forest; Single nucleotide polymorphism.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Validation Study

MeSH terms

Algorithms
Alternative Splicing
Disease / genetics*
Exons
Gene Frequency
Genetic Techniques*
Humans
Introns*
Models, Genetic*
Polymorphism, Single Nucleotide*
Software

Abstract

Publication types

MeSH terms

Grants and funding