SCISSOR: a framework for identifying structural changes in RNA transcripts

Hyo Young Choi; Heejoon Jo; Xiaobei Zhao; Katherine A. Hoadley; Scott Newman; Jeremiah Holt; Michele C. Hayward; Michael I. Love; J. S. Marron; D. Neil Hayes

doi:10.1038/s41467-020-20593-3

Nature Communications (Jan 2021)

SCISSOR: a framework for identifying structural changes in RNA transcripts

Hyo Young Choi,
Heejoon Jo,
Xiaobei Zhao,
Katherine A. Hoadley,
Scott Newman,
Jeremiah Holt,
Michele C. Hayward,
Michael I. Love,
J. S. Marron,
D. Neil Hayes

Affiliations

Hyo Young Choi: Department of Medicine, University of Tennessee Health Science Center
Heejoon Jo: Department of Medicine, University of Tennessee Health Science Center
Xiaobei Zhao: Department of Medicine, University of Tennessee Health Science Center
Katherine A. Hoadley: Department of Genetics, University of North Carolina
Scott Newman: Department of Computational Biology, St. Jude Children’s Research Hospital
Jeremiah Holt: Department of Medicine, University of Tennessee Health Science Center
Michele C. Hayward: Lineberger Comprehensive Cancer Center, University of North Carolina
Michael I. Love: Department of Genetics, University of North Carolina
J. S. Marron: Department of Statistics and Operations Research, University of North Carolina
D. Neil Hayes: Department of Medicine, University of Tennessee Health Science Center

DOI: https://doi.org/10.1038/s41467-020-20593-3
Journal volume & issue: Vol. 12, no. 1
pp. 1 – 12

Abstract

Read online

Many computational tools identify mRNA variations by analyzing the transformed RNA-seq data such as collapsed reads. Here, the authors report a computational method which uses shape changes in the RNA-seq coverage profile to discover changes in mRNA expression and alternative splicing.

Published in Nature Communications

ISSN: 2041-1723 (Online)
Publisher: Nature Portfolio
Country of publisher: United Kingdom
LCC subjects: Science
Website: https://www.nature.com/ncomms/

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