Detecting and visualizing gene fusions

• Published in: Methods (San Diego, Calif.) Calif.), 2013-01, Vol.59 (1), p.S24-S28
• Main Authors: Supper, Jochen, Gugenmus, Claudia, Wollnik, Johannes, Drueke, Tanja, Scherf, Matthias, Hahn, Alexander, Grote, Korbinian, Bretschneider, Nancy, Klocke, Bernward, Zinser, Christian, Cartharius, Kerstin, Seifert, Martin
• Format: Article
• Language: English
• Subjects: biomarkers; Biomarkers, Tumor - genetics; Breast cancer; Cell Line, Tumor; Chromosome Mapping - methods; copy number; Data processing; DNA Copy Number Variations; Gene expression; Gene Expression Profiling; Gene fusion; Genomes; High-Throughput Nucleotide Sequencing; Humans; Molecular Sequence Annotation - methods; Oncogene Proteins, Fusion - genetics; Sequence Analysis, DNA; Single-nucleotide polymorphism; Transcription; Tumor cell lines
• ISSN: 1046-2023; 1095-9130
• DOI: 10.1016/j.ymeth.2012.09.013

In recent years, gene fusions have gained significant recognition as biomarkers. They can assist treatment decisions, are seldom found in normal tissue and are detectable through Next-generation sequencing (NGS) of the transcriptome (RNA-seq). To transform the data provided by the sequencer into robust gene fusion detection several analysis steps are needed. Usually the first step is to map the sequenced transcript fragments (RNA-seq) to a reference genome. One standard application of this approach is to estimate expression and detect variants within known genes, e.g. SNPs and indels. In case of gene fusions, however, completely novel gene structures have to be detected. Here, we describe the detection of such gene fusion events based on our comprehensive transcript annotation (ElDorado). To demonstrate the utility of our approach, we extract gene fusion candidates from eight breast cancer cell lines, which we compare to experimentally verified gene fusions. We discuss several gene fusion events, like BCAS3–BCAS4 that was only detected in the breast cancer cell line MCF7. As supporting evidence we show that gene fusions occur more frequently in copy number enriched regions (CNV analysis). In addition, we present the Transcriptome Viewer (TViewer) a tool that allows to interactively visualize gene fusions. Finally, we support detected gene fusions through literature mining based annotations and network analyses. In conclusion, we present a platform that allows detecting gene fusions and supporting them through literature knowledge as well as rich visualization capabilities. This enables scientists to better understand molecular processes, biological functions and disease associations, which will ultimately lead to better biomedical knowledge for the development of biomarkers for diagnostics and therapies.