Identification of genomic indels and structural variations using split reads.
Identification of genomic indels and structural variations using split reads.
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DOI：
10.1186/1471-2164-12-375
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发表时间：
2011-07-25
期刊：
影响因子：
4.4
通讯作者：
Gerstein M
中科院分区：
文献类型：
--
作者：
Zhang ZD;Du J;Lam H;Abyzov A;Urban AE;Snyder M;Gerstein M
最近的研究证明了插入、缺失和其他更复杂的结构变异 (SV) 在人类中的遗传意义。随着新一代测序技术的发展，全基因组水平上的SV高通量研究已成为可能。在这里，我们提出了分割读取识别、校准 (SRiC)，这是一种基于序列的 SV 检测方法。我们首先使用缺口比对以标准方式将每个读数映射到参考基因组。然后为了识别 SV，我们使用一种评估策略对许多初始映射中的每一个进行评分，该评估策略旨在考虑测序和比对错误（例如，对读取中心有间隙的事件进行更高的评分）。由于实验和计算的限制（例如，调用的删除多于插入），所有当前的 SV 调用方法在识别中都存在多级偏差。我们方法的一个关键方面是，我们根据高通量测序模拟生成的合成数据集（使用真实的错误模型）来校准所有调用。这使我们能够计算不同参数值场景和不同类别事件（例如长删除与短插入）下的灵敏度和阳性预测值。我们对千人基因组计划的代表性数据进行计算。将观察到的 1 号染色体上的事件数量与从模拟中收集的校准（针对不同长度的事件）结合起来，使我们能够对人类基因组中跨各种长度尺度的 SV 总数构建相对无偏的估计。我们特别估计单个基因组包含约 670,000 个插入缺失/SV。与现有的用于 SV 识别的读长深度和读对方法相比，我们的方法可以精确定位 SV 事件的确切断点，揭示插入的实际序列内容，并覆盖删除的整个大小范围。此外，随着产生更长读长的第三代测序技术的出现，我们期望我们的方法更加有用。
Recent studies have demonstrated the genetic significance of insertions, deletions, and other more complex structural variants (SVs) in the human population. With the development of the next-generation sequencing technologies, high-throughput surveys of SVs on the whole-genome level have become possible. Here we present split-read identification, calibrated (SRiC), a sequence-based method for SV detection. We start by mapping each read to the reference genome in standard fashion using gapped alignment. Then to identify SVs, we score each of the many initial mappings with an assessment strategy designed to take into account both sequencing and alignment errors (e.g. scoring more highly events gapped in the center of a read). All current SV calling methods have multilevel biases in their identifications due to both experimental and computational limitations (e.g. calling more deletions than insertions). A key aspect of our approach is that we calibrate all our calls against synthetic data sets generated from simulations of high-throughput sequencing (with realistic error models). This allows us to calculate sensitivity and the positive predictive value under different parameter-value scenarios and for different classes of events (e.g. long deletions vs. short insertions). We run our calculations on representative data from the 1000 Genomes Project. Coupling the observed numbers of events on chromosome 1 with the calibrations gleaned from the simulations (for different length events) allows us to construct a relatively unbiased estimate for the total number of SVs in the human genome across a wide range of length scales. We estimate in particular that an individual genome contains ~670,000 indels/SVs. Compared with the existing read-depth and read-pair approaches for SV identification, our method can pinpoint the exact breakpoints of SV events, reveal the actual sequence content of insertions, and cover the whole size spectrum for deletions. Moreover, with the advent of the third-generation sequencing technologies that produce longer reads, we expect our method to be even more useful.
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通过种群规模的基因组测序来映射拷贝数变化。
影响因子：
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Mills, Ryan E.;Walter, Klaudia;Stewart, Chip;Handsaker, Robert E.;Chen, Ken;Alkan, Can;Abyzov, Alexej;Yoon, Seungtai Chris;Ye, Kai;Cheetham, R. Keira;Chinwalla, Asif;Conrad, Donald F.;Fu, Yutao;Grubert, Fabian;Hajirasouliha, Iman;Hormozdiari, Fereydoun;Iakoucheva, Lilia M.;Iqbal, Zamin;Kang, Shuli;Kidd, Jeffrey M.;Konkel, Miriam K.;Korn, Joshua;Khurana, Ekta;Kural, Deniz;Lam, Hugo Y. K.;Leng, Jing;Li, Ruiqiang;Li, Yingrui;Lin, Chang-Yun;Luo, Ruibang;Mu, Xinmeng Jasmine;Nemesh, James;Peckham, Heather E.;Rausch, Tobias;Scally, Aylwyn;Shi, Xinghua;Stromberg, Michael P.;Stuetz, Adrian M.;Urban, Alexander Eckehart;Walker, Jerilyn A.;Wu, Jiantao;Zhang, Yujun;Zhang, Zhengdong D.;Batzer, Mark A.;Ding, Li;Marth, Gabor T.;McVean, Gil;Sebat, Jonathan;Snyder, Michael;Wang, Jun;Ye, Kenny;Eichler, Evan E.;Gerstein, Mark B.;Hurles, Matthew E.;Lee, Charles;McCarroll, Steven A.;Korbel, Jan O.
通讯作者：
Korbel, Jan O.