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Comparative Study
. 2016 Feb 10:6:21597.
doi: 10.1038/srep21597.

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

Shailesh Kumar  1 , Angie Duy Vo  1 , Fujun Qin  1 , Hui Li  1   2
Affiliations
Comparative Study

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

Shailesh Kumar et al. Sci Rep. .

Abstract

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, we compared the performance of 12 well-known fusion detection software packages. We evaluated the sensitivity, false discovery rate, computing time, and memory usage of these tools in four different datasets (positive, negative, mixed, and test). We conclude that some tools are better than others in terms of sensitivity, positive prediction value, time consumption and memory usage. We 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. We have found that the performance of the tools depends on the quality, read length, and number of reads of the RNA-Seq data. We recommend that users choose the proper tools for their purpose based on the properties of their RNA-Seq data.

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Figures

Figure 1
Figure 1. Comparison of time and computational memory used by software packages on the positive dataset.
BE: Bellerophontes, CH: Chimerascan, ER: EricScript, NF: nFuse, FC: FusionCatcher, FH: FusionHunter, FM: FusionMap, JA: JAFFA, MS: MapSplice, SF: SOAPfuse, TF: TopHat-Fusion.
Figure 2
Figure 2. Common false fusions present in the negative dataset.
(a) FusionMap, (b) nFuse and (c) MapSplice.
Figure 3
Figure 3. 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.
Figure 4
Figure 4. TOPSIS score comparison of the tools.
(a) TOPSIS scores calculated by giving equal weight to Sensitivity, RAM, Time and PPV, (b) TOPSIS scores calculated by giving more weight (i.e. 0.35) to Sensitivity and PPV; and less weight (i.e. 0.15) to RAM and time. BE: Bellerophontes, ER: EricScript, FC: FusionCatcher, FH: FusionHunter, FM: FusionMap, JA: JAFFA, MS: MapSplice, NF: nFuse, TF: TopHat-Fusion.
See this image and copyright information in PMC

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