SCISSOR: a framework for identifying structural changes in RNA transcripts

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.

Prioritizing risk genes as novel stratification biomarkers for acute monocytic leukemia by integrative analysis

“…6D and E). Using an algorithm (SCISSOR) for calculating the risk score of each single cell [27], we also validated that monocytic leukemic cells rather than leukemic progenitors are profoundly deleterious for diseases progression, as assessed by CD34/CD11b labeling and the in silico inferring (Fig. 6F).…”

Whole-genome characterization of lung adenocarcinomas lacking alterations in the RTK/RAS/RAF pathway

“…RNA-seq BAM files for TCGA LUAD samples were used as input to SCISSOR (Choi et al, 2021) for analysis of structural changes in RNA transcripts. Briefly, SCISSOR is a statistical method for unsupervised screening of a range of structural alterations in RNA-seq data including alternative splicing, intron retention, de novo splice sites, intra-/intergenic deletions, and alternative transcription start/ termination.…”

High-resolution single-cell atlas reveals diversity and plasticity of tissue-resident neutrophils in non-small cell lung cancer

“…The TCGA reference dataset includes this information together with bulk RNA-sequencing data. Using the recently published computational method SCISSOR (Choi et al, 2021) we evaluated the association of atlas-derived cell type transcriptomic signatures with genotype and survival data from the TCGA reference dataset including 1026 patients (UICC I-IV, LUAD and LUSC). In a previous pan-cancer study using bulk RNAseq data we have shown that genomic features including mutational load, tumor heterogeneity, and specific driver genes determine immune phenotypes (Charoentong et al, 2017).…”