Comparative assessment of methods for the fusion transcripts detection from RNA-Seq data

RNA-Seq made possible the global identification of fusion transcripts, i.e. “chimeric RNAs”. Even though various software packages have been developed to serve this purpose, they behave differently in different datasets provided by different developers. It is important for both users, and developers to have an unbiased assessment of the performance of existing fusion detection tools.

Toward this goal, researchers from the University of Virginia compared the performance of 12 well-known fusion detection software packages.

A complete summary of 12 fusion-detection tools

Tool Name Group Reference URL Year
Bellerophontes Paired-end + Fragmentation Bioinformatics. (Abate et al.25) 2012
BreakFusion Whole paired-end Bioinformatics. (Chen et al.18) 2012
ChimeraScan Paired-end + Fragmentation Bioinformatics. (Iyer et al.29) 2011
EricScript Whole paired-end Bioinformatics. (Benelli et al.22) 2012
FusionCatcher Paired-end + Fragmentation bioRxiv. (Nicorici et al.23) 2012
FusionHunter Whole paired-end Bioinformatics. (Li et al.12) 2011
FusionMap Direct Fragmentation Bioinformatics. (Ge et al.13) 2011
JAFFA Paired-end + single-end Genome Medicine. (Davidson et al.20) 2015
MapSplice Direct Fragmentation Nucleic Acids. Research (Wang et al.15) 2010
nFuse Whole paired-end Genome research. (McPherson et al.21) 2012
SOAPFuse Whole paired-end Genome biology. (Jia et al.19) 2013
TopHat-Fusion Paired-end + Fragmentation Genome biology. (Kim and Salzberg, 2011) 2011


The researchers evaluated the sensitivity, false discovery rate, computing time, and memory usage of these tools in four different datasets (positive, negative, mixed, and test). They conclude that some tools are better than others in terms of sensitivity, positive prediction value, time consumption and memory usage. The researchers also observed small overlaps of the fusions detected by different tools in the real dataset (test dataset). This could be due to false discoveries by various tools, but could also be due to the reason that none of the tools are inclusive. They have found that the performance of the tools depends on the quality, read length, and number of reads of the RNA-Seq data.

Comparison of computational time and memory used by software packages on the test dataset


(a) Times consumed (Minutes) by the software packages to analyse each run of test dataset, (b) Computational Memory (GB) used by the software packages to analyse each run of test dataset. BE: Bellerophontes, CH: Chimerascan, ER: EricScript, NF: nFuse, FC: FusionCatcher, FH: FusionHunter, FM: FusionMap, JA: JAFFA, MS: MapSplice, SF: SOAPfuse, TF: TopHat-Fusion.

Kumar S, Vo AD, Qin F, Li H. (2016) Comparative assessment of methods for the fusion transcripts detection from RNA-Seq data. Sci Rep 6:21597. [article]

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