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

Research output: Contribution to journalArticle

1 Scopus citations

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.

Original languageEnglish (US)
Article number254
JournalGenome biology
Volume20
Issue number1
DOIs
StatePublished - Nov 28 2019

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Keywords

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

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Genetics
  • Cell Biology

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