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
|Bellerophontes||Paired-end + Fragmentation||Bioinformatics. (Abate et al.25)||http://eda.polito.it/bellerophontes/||2012|
|BreakFusion||Whole paired-end||Bioinformatics. (Chen et al.18)||http://bioinformatics.mdanderson.org/main/BreakFusion||2012|
|ChimeraScan||Paired-end + Fragmentation||Bioinformatics. (Iyer et al.29)||http://code.google.com/p/chimerascan/||2011|
|EricScript||Whole paired-end||Bioinformatics. (Benelli et al.22)||http://sourceforge.net/projects/ericscript/||2012|
|FusionCatcher||Paired-end + Fragmentation||bioRxiv. (Nicorici et al.23)||http://code.google.com/p/fusioncatcher/||2012|
|FusionHunter||Whole paired-end||Bioinformatics. (Li et al.12)||http://bioen-compbio.bioen.illinois.edu/FusionHunter/||2011|
|FusionMap||Direct Fragmentation||Bioinformatics. (Ge et al.13)||http://www.arrayserver.com/wiki/index.php?title=FusionMap||2011|
|JAFFA||Paired-end + single-end||Genome Medicine. (Davidson et al.20)||https://github.com/Oshlack/JAFFA/wiki||2015|
|MapSplice||Direct Fragmentation||Nucleic Acids. Research (Wang et al.15)||http://www.netlab.uky.edu/p/bioinfo/MapSplice||2010|
|nFuse||Whole paired-end||Genome research. (McPherson et al.21)||https://code.google.com/p/nfuse/||2012|
|SOAPFuse||Whole paired-end||Genome biology. (Jia et al.19)||http://soap.genomics.org.cn/soapfuse.html||2013|
|TopHat-Fusion||Paired-end + Fragmentation||Genome biology. (Kim and Salzberg, 2011)||http://tophat.cbcb.umd.edu/fusion_index.html||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.