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RNA-seq Fusion Detection in Clinical Oncology

Part of the Advances in Experimental Medicine and Biology book series (AEMB,volume 1361)


Gene fusions play a prominent role in the oncogenesis of many cancers and have been extensively targeted as biomarkers for diagnostic, prognostic, and therapeutic purposes. Detection methods span a number of platforms, including cytogenetics (e.g., FISH), targeted qPCR, and sequencing-based assays. Before the advent of next-generation sequencing (NGS), fusion testing was primarily targeted to specific genome loci, with assays tailored for previously characterized fusion events. The availability of whole genome sequencing (WGS) and whole transcriptome sequencing (RNA-seq) allows for genome-wide screening for the simultaneous detection of both known and novel fusions. RNA-seq, in particular, offers the possibility of rapid turn-around testing with less dedicated sequencing than WGS. This makes it an attractive target for clinical oncology testing, particularly when transcriptome data can be multi-purposed for tumor classification and additional analyses. Despite considerable efforts and substantial progress, however, genome-wide screening for fusions solely based on RNA-seq data remains an ongoing challenge. A host of technical artifacts adversely impact the sensitivity and specificity of existing software tools. In this chapter, the general strategies employed by current fusion software are discussed, and a selection of available fusion detection tools are surveyed. Despite its current limitations, RNA-seq-based fusion detection offers a more comprehensive and efficient strategy as compared to multiple targeted fusion assays. When thoughtfully employed within a wider ecosystem of diagnostic assays and clinical information, RNA-seq fusion detection represents a powerful tool for precision oncology.


  • Fusion
  • Screening
  • RNA-seq
  • Chimeric
  • Junction
  • Breakpoint
  • Soft clip
  • Internal tandem duplication (ITD)
  • Sequencing artifact
  • Discordant reads
  • Alignment
  • Mapping
  • Homology
  • Sensitivity
  • Specificity
  • Ensemble

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Correspondence to Dale J. Hedges .

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Hedges, D.J. (2022). RNA-seq Fusion Detection in Clinical Oncology. In: Laganà, A. (eds) Computational Methods for Precision Oncology. Advances in Experimental Medicine and Biology, vol 1361. Springer, Cham.

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