Computational approach to discriminate human and mouse sequences in patient-derived tumour xenografts.
Computational approach to discriminate human and mouse sequences in patient-derived tumour xenografts.
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DOI：
10.1186/s12864-017-4414-y
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发表时间：
2018-01-05
期刊：
影响因子：
4.4
通讯作者：
Caldas C
中科院分区：
文献类型：
--
作者：
Callari M;Batra AS;Batra RN;Sammut SJ;Greenwood W;Clifford H;Hercus C;Chin SF;Bruna A;Rueda OM;Caldas C
患者源性肿瘤异种移植（PDTXs）已成为最能代表临床肿瘤多样性和肿瘤内异质性的临床前模型。利用高通量测序（High-Throughput Sequencing， HTS）对PDTXs进行分子表征至关重要；然而，小鼠间质的存在对HTS数据分析具有挑战性。事实上，两个基因组之间的高度同源性导致老鼠的一部分读取被映射为人类。在这项研究中，我们从已知的小鼠和人类DNA或RNA混合物的样本以及人类乳腺癌及其衍生的PDTXs队列中获得了全外显子组测序（WES）、减少亚硫酸氢盐测序（RRBS）和RNA测序（RNA-seq）数据。研究结果表明，使用人机组合参考基因组（ICRG）对人类和小鼠的基因组序列进行比对，准确率高达99.9%，并减少了由>引起的假阳性体细胞突变的数量99.9%。我们还推导了一个模型来估计独立PDTX样品中的人类DNA含量。对于RNA-seq和RRBS数据分析，ICRG的使用允许对人类肿瘤细胞和小鼠基质的转录组和甲基组进行计算解剖。在与先前报道的方法的直接比较中，我们的方法显示出相似或更高的精度，同时需要更少的计算时间。我们在这里描述的计算管道是一个有价值的工具，用于分子分析PDTXs以及任何其他DNA或RNA物种的混合物。本文的在线版本（10.1186/s12864-017-4414-y）包含补充材料，授权用户可使用。
Patient-Derived Tumour Xenografts (PDTXs) have emerged as the pre-clinical models that best represent clinical tumour diversity and intra-tumour heterogeneity. The molecular characterization of PDTXs using High-Throughput Sequencing (HTS) is essential; however, the presence of mouse stroma is challenging for HTS data analysis. Indeed, the high homology between the two genomes results in a proportion of mouse reads being mapped as human. In this study we generated Whole Exome Sequencing (WES), Reduced Representation Bisulfite Sequencing (RRBS) and RNA sequencing (RNA-seq) data from samples with known mixtures of mouse and human DNA or RNA and from a cohort of human breast cancers and their derived PDTXs. We show that using an In silico Combined human-mouse Reference Genome (ICRG) for alignment discriminates between human and mouse reads with up to 99.9% accuracy and decreases the number of false positive somatic mutations caused by misalignment by >99.9%. We also derived a model to estimate the human DNA content in independent PDTX samples. For RNA-seq and RRBS data analysis, the use of the ICRG allows dissecting computationally the transcriptome and methylome of human tumour cells and mouse stroma. In a direct comparison with previously reported approaches, our method showed similar or higher accuracy while requiring significantly less computing time. The computational pipeline we describe here is a valuable tool for the molecular analysis of PDTXs as well as any other mixture of DNA or RNA species. The online version of this article (10.1186/s12864-017-4414-y) contains supplementary material, which is available to authorized users.
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影响因子：
82.9
作者：
Gao, Hui;Korn, Joshua M.;Sellers, William R.
通讯作者：
Sellers, William R.
DOI：
10.1093/bioinformatics/btu638
发表时间：
2015-01-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Anders S;Pyl PT;Huber W
通讯作者：
Huber W
影响因子：
30.8
作者：
Zack, Travis I.;Schumacher, Steven E.;Carter, Scott L.;Cherniack, Andrew D.;Saksena, Gordon;Tabak, Barbara;Lawrence, Michael S.;Zhang, Cheng-Zhong;Wala, Jeremiah;Mermel, Craig H.;Sougnez, Carrie;Gabriel, Stacey B.;Hernandez, Bryan;Shen, Hui;Laird, Peter W.;Getz, Gad;Meyerson, Matthew;Beroukhim, Rameen
通讯作者：
Beroukhim, Rameen
影响因子：
12.3
作者：
Callari M;Sammut SJ;De Mattos-Arruda L;Bruna A;Rueda OM;Chin SF;Caldas C
通讯作者：
Caldas C
影响因子：
8.8
作者：
Tufegdzic Vidakovic A;Rueda OM;Vervoort SJ;Sati Batra A;Goldgraben MA;Uribe-Lewis S;Greenwood W;Coffer PJ;Bruna A;Caldas C
通讯作者：
Caldas C