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OncodriveFML: a general framework to identify coding and non-coding regions with cancer driver mutations

机译:OncodriveFML:识别具有癌症驱动突变的编码区和非编码区的通用框架

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摘要

Distinguishing the driver mutations from somatic mutations in a tumor genome is one of the major challenges of cancer research. This challenge is more acute and far from solved for non-coding mutations. Here we present OncodriveFML, a method designed to analyze the pattern of somatic mutations across tumors in both coding and non-coding genomic regions to identify signals of positive selection, and therefore, their involvement in tumorigenesis. We describe the method and illustrate its usefulness to identify protein-coding genes, promoters, untranslated regions, intronic splice regions, and lncRNAs-containing driver mutations in several malignancies.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-016-0994-0) contains supplementary material, which is available to authorized users.
机译:在肿瘤基因组中区分驾驶员突变与体细胞突变是癌症研究的主要挑战之一。对于非编码突变,这一挑战更为严峻,远未解决。在这里,我们介绍OncodriveFML,该方法旨在分析编码和非编码基因组区域中跨肿瘤的体细胞突变模式,以鉴定阳性选择信号,从而确定其参与肿瘤发生的能力。我们描述了该方法并说明了其在几种恶性肿瘤中识别蛋白质编码基因,启动子,非翻译区,内含子剪接区和含lncRNA的驱动程序突变的有用性。电子补充材料本文的在线版本(doi:10.1186 / s13059-016 -0994-0)包含补充材料,授权用户可以使用。

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