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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. ORCID: https://orcid.org/0000-0002-8943-8484, 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: 25 Nov 2022 11:56
URI: https://orca.cardiff.ac.uk/id/eprint/127348

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