Author’s Comment: ArtiFuse – Computational validation of fusion gene detection tools without relying on simulated reads

TRON is thrilled to announce an article published in Bioinformatics. Author Patrick Sorn shares this Author’s Comment.

Fusion genes, resulting from larger chromosomal rearrangements, can play an important role in the development of cancer. Investigating such events is hence not only essential in understanding cancer biology but may help identify therapeutic targets. Unfortunately, the performance of existing fusion detection tools cannot be evaluated due to the lack of known fusion events. In the past, simulated reads that form such fusion events during alignment have been used to assess the performance of the tools. However, read simulation cannot represent the biological complexity of RNA-seq data.

In this article, we present a method to introduce artificial fusion events into the chromosomal sequences of the human reference genome. Using a dedicated set of fusion detection tools on MCF7 samples, we compared our approach with read simulation data and show that only our tool, ArtiFuse, incorporates the biological variety of sequencing data.

The ArtiFuse approach can be used to benchmark the performance of published fusion detection tools and helps to build up a repertoire of high-quality tools for upcoming analyses. 

You can read the article here.