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PSSV: a novel pattern-based probabilistic approach for somatic structural variation identification

机译:PSSV:一种用于体细胞结构变异识别的新型模式的概率方法

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Motivation: Whole genome DNA-sequencing (WGS) of paired tumor and normal samples has enabled the identification of somatic DNA changes in an unprecedented detail. Large-scale identification of somatic structural variations (SVs) for a specific cancer type will deepen our understanding of driver mechanisms in cancer progression. However, the limited number of WGS samples, insufficient read coverage, and the impurity of tumor samples that contain normal and neoplastic cells, limit reliable and accurate detection of somatic SVs.
机译:动机:配对肿瘤和正常样品的全基因组DNA测序(WGS)使得能够以前所未有的细节鉴定体细胞DNA变化。 特异性癌症类型的体细胞结构变化(SVS)的大规模鉴定将深化我们对癌症进展中驾驶员机制的理解。 然而,有限数量的WGS样本,读取覆盖不足,肿瘤样品的杂质,含有正常和肿瘤细胞,限制可靠和精确地检测体细胞体SV。

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