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

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

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 i...

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Bibliographic Details
Main Authors: Hai Lin, Katherine A. Hargreaves, Rudong Li, Jill L. Reiter, Yue Wang, Matthew Mort, David N. Cooper, Yaoqi Zhou, Chi Zhang, Michael T. Eadon, M. Eileen Dolan, Joseph Ipe, Todd C. Skaar, Yunlong Liu
Format: Article
Language:English
Published: BMC 2019-11-01
Series:Genome Biology
Subjects:
Intron
Single nucleotide polymorphism
RNA splicing
Computational biology
Bioinformatics
Disease pathogenesis
Online Access:https://doi.org/10.1186/s13059-019-1847-4

Internet

https://doi.org/10.1186/s13059-019-1847-4

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