Accurate and Effective Detection of Recurrent Copy Number Variants in Large SNP Genotype Datasets.
Accurate and Effective Detection of Recurrent Copy Number Variants in Large SNP Genotype Datasets.
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DOI：
10.1002/cpz1.621
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发表时间：
2022-12
期刊：
影响因子：
--
通讯作者：
Ingason, Andres
中科院分区：
文献类型：
--
作者：
Montalbano, Simone;Sanchez, Xabier Calle;Vaez, Morteza;Helenius, Dorte;Werge, Thomas;Ingason, Andres
结构变异，包括特定基因组位点的重复拷贝数变异（CNV），已被发现与几种疾病和综合征的风险增加有关。CNV携带者状态可以使用SNP阵列和最近的测序数据在大量样本中确定。虽然研究人员对这种分析所需的基本步骤有一些共识（即，CNV检测、推定携带者的过滤和使用基因组区域的强度数据图的视觉验证），缺乏控制结果的质量和一致性的标准方法和过程。在这里，我们提出了一个全面和用户友好的协议，我们已经从我们在该领域的广泛研究经验中提炼出来。我们涵盖了分析的各个方面，从输入数据管理到最终结果。对于每一步，我们强调哪些参数对分析影响最大，以及不同的设置如何导致不同的结果。我们提供了一个管道来运行有效的（但可定制的）预设的完整分析。我们提出了我们开发的软件，以更好地处理和过滤推定的CNV载体，并进行目视检查，以验证选定的候选人。最后，我们描述的方法来评估的关键部分和行动，以平衡潜在的问题。目前的实现集中在Illumina SNP阵列数据上。所有呈现的软件都是免费提供的，并在一个现成的Docker容器中提供。从强度数据文件到CNV调用。执行SNP过滤，PennCNV调用管道，应用光后处理，tabix索引强度文件。从CNV呼叫到经过验证的CNV携带者。过滤选定基因座中的推定CNV载体，进行目视验证，并导出结果。质量控制运行一系列全面的测试和指导，如果出现任何问题，可以更改哪些参数。安装必要的软件。
Structural Variations, including recurrent Copy Number Variants (CNVs) at specific genomic loci, have been found to be associated with increased risk of several diseases and syndromes. CNV carrier status can be determined in large collections of samples using SNP-arrays and, more recently, sequencing data. Although there is some consensus among researchers about the essential steps required in such analysis (i.e., CNV Calling, Filtering of putative carriers, and Visual Validation using intensity data plots of the genomic region), standard methodologies and processes to control the quality and consistency of the results are lacking. Here, we present a comprehensive and user-friendly protocol that we have refined from our extensive research experience in the field. We cover every aspect of the analysis, from input data curation to final results. For each step we highlight which parameters affect the analysis the most and how different settings may lead to different results. We provide a pipeline to run the complete analysis with effective (but customizable) pre-sets. We present software that we developed to better handle and filter putative CNV carriers and perform visual inspection to validate selected candidates. Finally, we describe methods to evaluate the critical sections and actions to counterbalance potential problems. The current implementation is focused on Illumina SNP-array data. All the presented software is freely available and provided in a ready-to-use docker container. From intensity data files to CNV calls. Perform SNP filtering, PennCNV calling pipeline, apply a light post-processing, tabix index intensity files. From CNVs calls to validated CNV carriers. Filter putative CNV carriers in the selected loci, perform visual validation, and export results. Quality control. Run a comprehensive series of tests and guidance on which parameters to change if any problem should arise. Install the necessary software.
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影响因子：
64.8
作者：
Sudmant PH;Rausch T;Gardner EJ;Handsaker RE;Abyzov A;Huddleston J;Zhang Y;Ye K;Jun G;Fritz MH;Konkel MK;Malhotra A;Stütz AM;Shi X;Casale FP;Chen J;Hormozdiari F;Dayama G;Chen K;Malig M;Chaisson MJP;Walter K;Meiers S;Kashin S;Garrison E;Auton A;Lam HYK;Mu XJ;Alkan C;Antaki D;Bae T;Cerveira E;Chines P;Chong Z;Clarke L;Dal E;Ding L;Emery S;Fan X;Gujral M;Kahveci F;Kidd JM;Kong Y;Lameijer EW;McCarthy S;Flicek P;Gibbs RA;Marth G;Mason CE;Menelaou A;Muzny DM;Nelson BJ;Noor A;Parrish NF;Pendleton M;Quitadamo A;Raeder B;Schadt EE;Romanovitch M;Schlattl A;Sebra R;Shabalin AA;Untergasser A;Walker JA;Wang M;Yu F;Zhang C;Zhang J;Zheng-Bradley X;Zhou W;Zichner T;Sebat J;Batzer MA;McCarroll SA;1000 Genomes Project Consortium;Mills RE;Gerstein MB;Bashir A;Stegle O;Devine SE;Lee C;Eichler EE;Korbel JO
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Turner, Daniel J.;Miretti, Marcos;Rajan, Diana;Fiegler, Heike;Carter, Nigel P.;Blayney, Martyn L.;Beck, Stephan;Hurles, Matthew E.
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Hurles, Matthew E.
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通讯作者：
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通讯作者：
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影响因子：
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作者：
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通讯作者：
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