RegSNPs-intron: a computational framework for predicting pathogenic impact of intronic single nucleotide variants
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Hargreaves, Katherine A
Li, Rudong
Reiter, Jill L
Wang, Yue
Mort, Matthew
Cooper, David N
Zhou, Yaoqi
Zhang, Chi
Eadon, Michael T
Dolan, M Eileen
Ipe, Joseph
Skaar, Todd C
Liu, Yunlong
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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.
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Genome Biology
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20
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1
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© 2019 The Author(s). This article is distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
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Environmental sciences
Biological sciences
Science & Technology
Life Sciences & Biomedicine
Biotechnology & Applied Microbiology
Genetics & Heredity
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Lin, H; Hargreaves, KA; Li, R; Reiter, JL; Wang, Y; Mort, M; Cooper, DN; Zhou, Y; Zhang, C; Eadon, MT; Dolan, ME; Ipe, J; Skaar, TC; Liu, Y, RegSNPs-intron: a computational framework for predicting pathogenic impact of intronic single nucleotide variants, Genome Biology, 2019, 20 (1)