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Your search keyword '"Katherine A. Hargreaves"' showing total 2 results
Start Over Author "Katherine A. Hargreaves" Publisher bmc
2 results on '"Katherine A. Hargreaves"'

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

Author

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, and Yunlong Liu

Subjects
Intron, Single nucleotide polymorphism, RNA splicing, Computational biology, Bioinformatics, Disease pathogenesis, Biology (General), QH301-705.5, Genetics, QH426-470
Abstract

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.

Published
2019
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2. RegSNPs-intron: a computational framework for predicting pathogenic impact of intronic single nucleotide variants

Author

Yunlong Liu, Michael T. Eadon, David Neil Cooper, Todd C. Skaar, Katherine A. Hargreaves, Yaoqi Zhou, Matthew Mort, M. Eileen Dolan, Chi Zhang, Yue Wang, Jill L. Reiter, Hai Lin, Rudong Li, and Joseph Ipe

Subjects
RNA splicing, lcsh:QH426-470, Bioinformatics, Disease pathogenesis, Intron, Method, Single-nucleotide polymorphism, Computational biology, Biology, Polymorphism, Single Nucleotide, High-throughput screening assay, Conserved sequence, 03 medical and health sciences, 0302 clinical medicine, Protein structure, Gene Frequency, Prediction model, Humans, Nucleotide, Disease, lcsh:QH301-705.5, 030304 developmental biology, chemistry.chemical_classification, 0303 health sciences, Reporter gene, Models, Genetic, Exons, Human genetics, Introns, Single nucleotide polymorphism, Alternative Splicing, lcsh:Genetics, chemistry, Genetic Techniques, lcsh:Biology (General), 030217 neurology & neurosurgery, Algorithms, Software, Random forest
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.

Published
2019

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