INTEGRATE: gene fusion discovery using whole genome and transcriptome data

Abstract

While next-generation sequencing (NGS) has become the primary technology for discovering gene fusions, we are still faced with the challenge of ensuring that causative mutations are not missed while minimizing false positives. Currently, there are many computational tools that predict structural variations (SV) and gene fusions using whole genome (WGS) and transcriptome sequencing (RNA-seq) data separately. However, as both WGS and RNA-seq have their limitations when used independently, we hypothesize that the orthogonal validation from integrating both data could generate a sensitive and specific approach for detecting high-confidence gene fusion predictions. Fortunately, decreasing NGS costs have resulted in a growing quantity of patients with both data available. Therefore, we developed a gene fusion discovery tool, INTEGRATE, that leverages both RNA-seq and WGS data to reconstruct gene fusion junctions and genomic breakpoints by split-read mapping.

Global analysis of somatic structural genomic alterations and their impact on gene expression in diverse human cancers

“…Although both DNA and RNA data have limitations when it comes to detecting fusions, we found that a combination of the two data types reduced the noise inherent to each approach. This is in agreement with recent studies that combine DNA and RNA evidence to improve detection of gene fusions (49,50).…”

GRIDSS: sensitive and specific genomic rearrangement detection using positional de Bruijn graph assembly

“…Finally, GRIDSS was run on DNA-seq data from the HCC1395 breast cancer cell line and results compared to the published genomic breakpoints, which were predicted to be associated with "validated" fusion genes (Zhang et al 2016). GRIDSS showed strong concordance with the published results (Supplemental Table S3).…”

Findings made in gene panel to whole genome sequencing: data, knowledge, ethics – and consequences?

“…Transcriptome analysis via RNA-seq is thus superior to gene panel or exomesequencing methods for detection of gene fusions [56]. However, the combination of WGS + RNA-seq with subsequent split-read analysis of sequencing reads [57] appears to be ideal for unbiased detection of break point at the genomic level [58] as well as their transcriptomic consequences [59]. It should be noted that chimeric RNAs of two genes are not necessarily associated with genomic rearrangements and/or disease, and some seem to have biological relevance [60,61].…”