ExonImpact: Prioritizing Pathogenic Alternative Splicing Events

Meng Li, Weixing Feng, Xinjun Zhang, Yuedong Yang, Kejun Wang, Matthew Mort, David N. Cooper, Yue Wang, Yaoqi Zhou, Yunlong Liu

Research output: Contribution to journalArticle

8 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)16-24
Number of pages9
JournalHuman Mutation
Issue number1
StatePublished - Jan 1 2017


  • alternative splicing
  • disease
  • exon impaction
  • machine learning

ASJC Scopus subject areas

  • Genetics
  • Genetics(clinical)

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    Li, M., Feng, W., Zhang, X., Yang, Y., Wang, K., Mort, M., Cooper, D. N., Wang, Y., Zhou, Y., & Liu, Y. (2017). ExonImpact: Prioritizing Pathogenic Alternative Splicing Events. Human Mutation, 38(1), 16-24. https://doi.org/10.1002/humu.23111