State-of-the-Art Fusion-Finder Algorithms Sensitivity and Specificity
State-of-the-Art Fusion-Finder Algorithms Sensitivity and Specificity
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DOI：
10.1155/2013/340620
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发表时间：
2013-01-01
影响因子：
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通讯作者：
Calogero, Raffaele A.
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文献类型：
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作者：
Carrara, Matteo;Beccuti, Marco;Calogero, Raffaele A.
背景。染色体易位引起的基因融合与癌症有关。 RNA-seq 有潜力发现这种重排产生功能性蛋白质（嵌合/融合）。最近，已经发表了许多嵌合体检测方法。然而，这些工具的特异性和敏感性并未以比较的方式进行广泛研究。结果。我们测试了八种融合检测工具（FusionHunter、FusionMap、FusionFinder、MapSplice、deFuse、Bellerophontes、ChimeraScan 和 TopHat-fusion），以使用包含嵌合体的合成和真实数据集来检测融合事件。仅在合成数据上运行的比较分析可能会产生误导性结果，因为我们在真实数据集上没有发现对应数据。此外，大多数工具报告大量假阳性嵌合体。特别是，最敏感的工具 ChimeraScan 报告了大量误报，我们可以通过设计和应用两个过滤器来去除不支持跨融合结读取或包含大内含子区域的融合，从而显着减少这些误报。结论。使用合成数据集和真实数据集获得的不一致结果表明，包含融合事件的合成数据集可能无法完全捕捉 RNA-seq 实验的复杂性。此外，融合检测工具的灵敏度或特异性仍然有限；因此，融合发现算法还有进一步改进的空间。
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.