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RegSNPs-intron: a computational framework for predicting pathogenic impact of intronic single nucleotide variants

Lin, Hai, 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. and Liu, Yunlong 2019. RegSNPs-intron: a computational framework for predicting pathogenic impact of intronic single nucleotide variants. Genome Biology 20 (1) , 254. 10.1186/s13059-019-1847-4

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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.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Medicine
Publisher: BioMed Central
ISSN: 1474-760X
Date of First Compliant Deposit: 5 December 2019
Date of Acceptance: 3 October 2019
Last Modified: 31 Jan 2020 22:53
URI: http://orca-mwe.cf.ac.uk/id/eprint/127348

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