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RNA Fusion Detection

Known and novel fusion detection

  • Fusion RNAs are a hallmark of some cancers. 


  • They result either from chromosomal rearrangements or from splicing mechanisms that are non-chromosomal rearrangements.

  • Chromosomal rearrangements that result in gene fusions are particularly prevalent in sarcomas and hematopoietic malignancies; they are also common in solid tumors.


  • Gene fusions can occur as a result of translocation, interstitial deletion, or chromosomal inversion.

RNA Fusion detection technology complements existing technologies

  • Fluorescence in situ hybridization (FISH) has been the gold standard for fusion detection, however it can only detect single mutations at a time and cannot detect novel fusion gene partners.


  • RNA-seq is another method for fusion detection, but has low sensitivity and a high read requirement as a result of whole transcriptome sequencing.

  • Paragon genomics have created a targeted multiplex fusion detection technology that can also detect novel fusion gene partners. Built upon CleanPlex® technology, this fusion technology allows for targeted interrogation of just the genes of interest in order to increase sensitivity, reduce non-specific reads, and allow accurate fusion detection in one simple workflow.


  • Dual primer multiplex PCR design

  • Targeted known fusion detection

  • Little amplification bias and high copy number correlation

  • AccuFusion RNA Lung Cancer Panel includes fusion genes associated with ALK, CIT, EML4, FGFR1, MBIP, MET, NRG1, NTRK1, NTRK3, PDGFRA, RET, ROS1, TACC3.

Confident detection of known and novel fusions using two routes


  • Single primer multiplex PCR design with template switching chemistry

  • Ability to detect novel fusion gene partners

  • Efficient sequencing with low rRNA rates

  • OmniFusion RNA Lung Cancer Panel includes fusion genes associated with 11 cancer driver genes ( ALK, CIT, MBIP, MET, NRG1, NTRK1, NTRK3, PDGFRA, RET, ROS1, TACC3).

Both methods are highly sensitive and compatible with low-quality RNA such as FFPE, and other sample types such as fresh/frozen tissue or cell lines. The chemistry also allows for minimal input samples and can generate robust libraries for confident calls from as little as 25ng (OmniFusion) or 10ng (AccuFusion).

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