Zheng Xiaoqi, Zhao Qian, Wu Hua-Jun, Li Wei, Wang Haiyun, Meyer Clifford A, Qin Qian Alvin, Xu Han, Zang Chongzhi, Jiang Peng, Li Fuqiang, Hou Yong, He Jianxing, Wang Jun, Wang Jun, Zhang Peng, Zhang Yong, Liu Xiaole Shirley
Genome Biol. 2014 Aug 7;15(8):419. doi: 10.1186/s13059-014-0419-x.
We propose a statistical algorithm MethylPurify that uses regions with bisulfite reads showing discordant methylation levels to infer tumor purity from tumor samples alone. MethylPurify can identify differentially methylated regions (DMRs) from individual tumor methylome samples, without genomic variation information or prior knowledge from other datasets. In simulations with mixed bisulfite reads from cancer and normal cell lines, MethylPurify correctly inferred tumor purity and identified over 96% of the DMRs. From patient data, MethylPurify gave satisfactory DMR calls from tumor methylome samples alone, and revealed potential missed DMRs by tumor to normal comparison due to tumor heterogeneity.
我们提出了一种统计算法MethylPurify，该算法利用亚硫酸氢盐测序读数显示甲基化水平不一致的区域，仅从肿瘤样本中推断肿瘤纯度。MethylPurify可以从单个肿瘤甲基化组样本中识别差异甲基化区域（DMR），而无需基因组变异信息或来自其他数据集的先验知识。在对来自癌症和正常细胞系的混合亚硫酸氢盐测序读数进行模拟时，MethylPurify正确推断出肿瘤纯度，并识别出超过96%的DMR。从患者数据来看，MethylPurify仅从肿瘤甲基化组样本中就给出了令人满意的DMR调用结果，并揭示了由于肿瘤异质性导致的肿瘤与正常样本比较中可能遗漏掉的DMR。
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