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 and D. Neil Hayes ()
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Hyo Young Choi: University of Tennessee Health Science Center
Heejoon Jo: University of Tennessee Health Science Center
Xiaobei Zhao: University of Tennessee Health Science Center
Katherine A. Hoadley: University of North Carolina
Scott Newman: St. Jude Children’s Research Hospital
Jeremiah Holt: University of Tennessee Health Science Center
Michele C. Hayward: University of North Carolina
Michael I. Love: University of North Carolina
J. S. Marron: University of North Carolina
D. Neil Hayes: University of Tennessee Health Science Center

Nature Communications, 2021, vol. 12, issue 1, 1-12

Abstract: Abstract High-throughput sequencing protocols such as RNA-seq have made it possible to interrogate the sequence, structure and abundance of RNA transcripts at higher resolution than previous microarray and other molecular techniques. While many computational tools have been proposed for identifying mRNA variation through differential splicing/alternative exon usage, challenges in its analysis remain. Here, we propose a framework for unbiased and robust discovery of aberrant RNA transcript structures using short read sequencing data based on shape changes in an RNA-seq coverage profile. Shape changes in selecting sample outliers in RNA-seq, SCISSOR, is a series of procedures for transforming and normalizing base-level RNA sequencing coverage data in a transcript independent manner, followed by a statistical framework for its analysis ( https://github.com/hyochoi/SCISSOR ). The resulting high dimensional object is amenable to unsupervised screening of structural alterations across RNA-seq cohorts with nearly no assumption on the mutational mechanisms underlying abnormalities. This enables SCISSOR to independently recapture known variants such as splice site mutations in tumor suppressor genes as well as novel variants that are previously unrecognized or difficult to identify by any existing methods including recurrent alternate transcription start sites and recurrent complex deletions in 3′ UTRs.

Date: 2021
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DOI: 10.1038/s41467-020-20593-3

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