Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2013:2013:340620.
doi: 10.1155/2013/340620. Epub 2013 Feb 17.

State-of-the-art fusion-finder algorithms sensitivity and specificity

Matteo Carrara  1 , Marco Beccuti, Fulvio Lazzarato, Federica Cavallo, Francesca Cordero, Susanna Donatelli, Raffaele A Calogero
Affiliations

State-of-the-art fusion-finder algorithms sensitivity and specificity

Matteo Carrara et al. Biomed Res Int. 2013.

Abstract

Background: 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.

Results: We 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. The comparison analysis run only on synthetic data could generate misleading results since we found no counterpart on 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 we were 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.

Conclusions: 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.

PubMed Disclaimer

Figures

Figure 1
Figure 1
Fusion events detection performances on positive data set encompassing 50 synthetic fusion events (FM_set). Total number of detected fusions is shown on the top of each bar set. FF: FusionFinder; THF: TopHat-fusion; MS: MapSplice; FM: FusionMap; FH: FusionHunter; DF: defuse; BF: Bellerophontes; CS: ChimeraScan.
Figure 2
Figure 2
Analysis of sensitivity of fusion finders in a real data set encompassing 27 validated fusions (Edgren_set). (a) Total number of detected fusions is shown on the top of each bar set. (b) and (c) Venn diagrams showing the overlaps between fusions founded by different tools. The ellipse of the ChimeraScan is highlighted in red. FF: FusionFinder; THF: TopHat-fusion; MS: MapSplice; FM: FusionMap; FH:FusionHunter; DF: defuse; BF: Bellerophontes; CS: ChimeraScan.
Figure 3
Figure 3
Analysis of sensitivity of fusion finders on a real data set encompassing 12 validated fusions (Berger_set). Total number of detected fusions is shown on the top of each set of bars. FM: FusionMap; FH: FusionHunter; DF: defuse; CS: ChimeraScan; THF: TopHat-fusion.
Figure 4
Figure 4
False positive fusion detected using a synthetic dataset without chimeras. FF: FusionFinder; THF: TopHat-fusion; MS: MapSplice; FM: FusionMap; FH: FusionHunter; DF: defuse; BF: Bellerophontes; CS: ChimeraScan.
See this image and copyright information in PMC

References

    1. Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nature Methods. 2008;5(7):621–628. - PubMed
    1. Maher CA, Kumar-Sinha C, Cao X, et al. Transcriptome sequencing to detect gene fusions in cancer. Nature. 2009;458(7234):97–101. - PMC - PubMed
    1. Abate F, Acquaviva A, Paciello G, et al. Bellerophontes: an RNA-Seq data analysis framework for chimeric transcripts discovery based on accurate fusion model. Bioinformatics. 2012;28(16):2114–2121. - PubMed
    1. Iyer MK, Chinnaiyan AM, Maher CA. ChimeraScan: a tool for identifying chimeric transcription in sequencing data. Bioinformatics. 2011;27(20):2903–2904. - PMC - PubMed
    1. McPherson A, Hormozdiari F, Zayed A, et al. deFuse: an algorithm for gene fusion discovery in tumor RNA-Seq data. PLoS Computational Biology. 2011;7(5)e1001138 - PMC - PubMed

Publication types

Substances

LinkOut - more resources