Team finds there is space for further improvement in the fusion-finder algorithms

Gene fusions arising from chromosomal translocations have been implicated in cancer. RNA-Seq has the potential to discover such rearrangements generating functional proteins (chimera/fusion). Recently, many methods for chimeras detection have been published. However, specificity and sensitivity of those tools were not extensively investigated in a comparative way.

In this study, a team of scientists at University of Torino, Italy tested eight fusion-detection tools (FusionHunter, FusionMap, FusionFinder, MapSplice, deFuse, Bellerophontes, ChimeraScan, and TopHat-fusion) to detect fusion events using synthetic and real datasets encompassing chimeras. They discovered that a comparison analysis run only on synthetic data could generate misleading results since they found no counterpart on a real dataset. Furthermore, most tools report a very high number of false positive chimeras. In particular, the most sensitive tool, ChimeraScan, reports a large number of false positives that the team was able to significantly reduce by devising and applying two filters to remove fusions not supported by fusion junction-spanning reads or encompassing large intronic regions.

Gen Fusions

The discordant results obtained using synthetic and real datasets suggest that synthetic datasets encompassing fusion events may not fully catch the complexity of RNA-Seq experiment. Moreover, fusion detection tools are still limited in sensitivity or specificity; thus, there is space for further improvement in the fusion-finder algorithms.

  • Carrara M, Beccuti M, Lazzarato F, Cavallo F, Cordero F, Donatelli S, Calogero RA. (2013) State-of-the-Art Fusion-Finder Algorithms Sensitivity and Specificity. BioMed Research International Vol 2013, Article ID 340620. [article]
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