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: | Schools > Medicine |
| Publisher: | BioMed Central |
| ISSN: | 1474-760X |
| Date of First Compliant Deposit: | 5 December 2019 |
| Date of Acceptance: | 3 October 2019 |
| Last Modified: | 05 May 2023 21:50 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/127348 |
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