SVExpress: identifying gene features altered recurrently in expression with nearby structural variant breakpoints

Yiqun Zhang,Fengju Chen,Chad J Creighton

doi:10.1186/s12859-021-04072-0

Abstract

BackgroundCombined whole-genome sequencing (WGS) and RNA sequencing of cancers offer the opportunity to identify genes with altered expression due to genomic rearrangements. Somatic structural variants (SVs), as identified by WGS, can involve altered gene cis-regulation, gene fusions, copy number alterations, or gene disruption. The absence of computational tools to streamline integrative analysis steps may represent a barrier in identifying genes recurrently altered by genomic rearrangement.ResultsHere, we introduce SVExpress, a set of tools for carrying out integrative analysis of SV and gene expression data. SVExpress enables systematic cataloging of genes that consistently show increased or decreased expression in conjunction with the presence of nearby SV breakpoints. SVExpress can evaluate breakpoints in proximity to genes for potential enhancer translocation events or disruption of topologically associated domains, two mechanisms by which SVs may deregulate genes. The output from any commonly used SV calling algorithm may be easily adapted for use with SVExpress. SVExpress can readily analyze genomic datasets involving hundreds of cancer sample profiles. Here, we used SVExpress to analyze SV and expression data across 327 cancer cell lines with combined SV and expression data in the Cancer Cell Line Encyclopedia (CCLE). In the CCLE dataset, hundreds of genes showed altered gene expression in relation to nearby SV breakpoints. Altered genes involved TAD disruption, enhancer hijacking, and gene fusions. When comparing the top set of SV-altered genes from cancer cell lines with the top SV-altered genes previously reported for human tumors from The Cancer Genome Atlas and the Pan-Cancer Analysis of Whole Genomes datasets, a significant number of genes overlapped in the same direction for both cell lines and tumors, while some genes were significant for cell lines but not for human tumors and vice versa.ConclusionOur SVExpress tools allow computational biologists with a working knowledge of R to integrate gene expression with SV breakpoint data to identify recurrently altered genes. SVExpress is freely available for academic or commercial use at https://github.com/chadcreighton/SVExpress. SVExpress is implemented as a set of Excel macros and R code. All source code (R and Visual Basic for Applications) is available.

Highlights

Combined whole-genome sequencing (WGS) and RNA sequencing of cancers offer the opportunity to identify genes with altered expression due to genomic rearrangements
The Cell Line Encyclopedia (CCLE) datasets were from the 2019 release [10], with gene-level RSEM expression calls by RNA sequencing (RNA-seq)
Somatic Structural variant (SV) calls were previously made in these cell lines by SVABA algorithm [10]

Summary

Introduction

Combined whole-genome sequencing (WGS) and RNA sequencing of cancers offer the opportunity to identify genes with altered expression due to genomic rearrangements. The absence of computational tools to streamline integrative analysis steps may represent a barrier in identifying genes recurrently altered by genomic rearrangement. Some of the individual steps involved in our SV-expression integrative analysis approach include constructing a gene-to-sample breakpoint pattern matrix, linear regression modeling to associate altered expression with nearby breakpoints, and identifying putative enhancer hijacking and TAD disruption events. These steps would be labor-intensive for most analysts working from scratch using standard tools such as R, Excel, or BEDtools [5]. The absence of computational tools to streamline these steps may represent a barrier to others’ ability to implement our approach in other datasets

Results

Discussion

Conclusion