ExonImpact: Prioritizing Pathogenic Alternative Splicing Events.
Alternative splicing (AS) is a closely regulated process that allows a single gene to encode multiple protein isoforms, thereby contributing to the diversity of the proteome. Dysregulation of the splicing process has been found to be associated with many inherited diseases. However, among the pathogenic AS events, there are numerous "passenger" events whose inclusion or exclusion does not lead to significant changes with respect to protein function. In this study, we evaluate the secondary and tertiary structural features of proteins associated with disease-causing and neutral AS events, and show that several structural features are strongly associated with the pathological impact of exon inclusion. We further develop a machine-learning-based computational model, ExonImpact, for prioritizing and evaluating the functional consequences of hitherto uncharacterized AS events. We evaluated our model using several strategies including cross-validation, and data from the Gene-Tissue Expression (GTEx) and ClinVar databases. ExonImpact is freely available at http://watson.compbio.iupui.edu/ExonImpact.
Institute of Intelligent System and Bioinformatics, College of Automation, Harbin Engineering University, Harbin, Heilongjiang, 150001, China.|Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA.
Meng
M
Li
Institute of Intelligent System and Bioinformatics, College of Automation, Harbin Engineering University, Harbin, Heilongjiang, 150001, China.
Weixing
W
Feng
Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA.
Xinjun
X
Zhang
Institute for Glycomics and School of Informatics and Communication Technology, Griffith University, Parklands Dr. Southport QLD 4215, Australia.
Yuedong
Y
Yang
Institute of Intelligent System and Bioinformatics, College of Automation, Harbin Engineering University, Harbin, Heilongjiang, 150001, China.
Kejun
K
Wang
Institute of Medical Genetics, Cardiff University, Heath Park, Cardiff CF14 4XN, UK.
Matthew
M
Mort
Institute of Medical Genetics, Cardiff University, Heath Park, Cardiff CF14 4XN, UK.
David N
DN
Cooper
Departments of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA.
Yue
Y
Wang
Institute for Glycomics and School of Informatics and Communication Technology, Griffith University, Parklands Dr. Southport QLD 4215, Australia.
Yaoqi
Y
Zhou
Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA.|Departments of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA.|Center for Medical Genomics, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA.
Yunlong
Y
Liu
D000465:Algorithms; D017398:Alternative Splicing; D001921:Brain / Q000378:metabolism; D019295:Computational Biology / Q000379:methods*; D030561:Databases, Nucleic Acid; D005091:Exons; D056726:Genetic Association Studies / Q000379:methods*; D020022:Genetic Predisposition to Disease; D006801:Humans; D000069550:Machine Learning; D000072417:Protein Domains; D020033:Protein Isoforms / Q000737:chemistry / Q000235:genetics / Q000378:metabolism; D012984:Software; D013329:Structure-Activity Relationship; D064878:Web Browser
D020033:Protein Isoforms
alternative splicing; disease; exon impaction; machine learning