RegSNPs-intron: a computational framework for predicting pathogenic impact of intronic single nucleotide variants
dc.contributor.author | Lin, Hai | |
dc.contributor.author | Hargreaves, Katherine A. | |
dc.contributor.author | Li, Rudong | |
dc.contributor.author | Reiter, Jill L. | |
dc.contributor.author | Wang, Yue | |
dc.contributor.author | Mort, Matthew | |
dc.contributor.author | Cooper, David N. | |
dc.contributor.author | Zhou, Yaoqi | |
dc.contributor.author | Zhang, Chi | |
dc.contributor.author | Eadon, Michael T. | |
dc.contributor.author | Dolan, M. Eileen | |
dc.contributor.author | Ipe, Joseph | |
dc.contributor.author | Skaar, Todd C. | |
dc.contributor.author | Liu, Yunlong | |
dc.contributor.department | Medical and Molecular Genetics, School of Medicine | en_US |
dc.date.accessioned | 2020-03-18T18:54:26Z | |
dc.date.available | 2020-03-18T18:54:26Z | |
dc.date.issued | 2019-11-28 | |
dc.description.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. | en_US |
dc.identifier.citation | Lin, H., Hargreaves, K. A., Li, R., Reiter, J. L., Wang, Y., Mort, M., ... & Dolan, M. E. (2019). RegSNPs-intron: a computational framework for predicting pathogenic impact of intronic single nucleotide variants. Genome biology, 20(1), 1-16. 10.1186/s13059-019-1847-4 | en_US |
dc.identifier.issn | 1474-760X | en_US |
dc.identifier.uri | https://hdl.handle.net/1805/22364 | |
dc.language.iso | en_US | en_US |
dc.publisher | BMC | en_US |
dc.relation.isversionof | 10.1186/s13059-019-1847-4 | en_US |
dc.relation.journal | Genome Biology | en_US |
dc.rights | Attribution 4.0 International | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
dc.source | PMC | en_US |
dc.subject | Intron | en_US |
dc.subject | Single nucleotide polymorphism | en_US |
dc.subject | RNA splicing | en_US |
dc.subject | Computational biology | en_US |
dc.subject | Bioinformatics | en_US |
dc.subject | Disease pathogenesis | en_US |
dc.subject | Random forest | en_US |
dc.subject | Prediction model | en_US |
dc.subject | High-throughput screening assay | en_US |
dc.title | RegSNPs-intron: a computational framework for predicting pathogenic impact of intronic single nucleotide variants | en_US |
dc.type | Article | en_US |
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