Optimized pipeline of MuTect and GATK tools to improve the detection of somatic single nucleotide polymorphisms in whole-exome sequencing data.
Optimized pipeline of MuTect and GATK tools to improve the detection of somatic single nucleotide polymorphisms in whole-exome sequencing data.
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DOI：
10.1186/s12859-016-1190-7
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发表时间：
2016-11-08
影响因子：
3
通讯作者：
Castellani G
中科院分区：
文献类型：
--
作者：
do Valle ÍF;Giampieri E;Simonetti G;Padella A;Manfrini M;Ferrari A;Papayannidis C;Zironi I;Garonzi M;Bernardi S;Delledonne M;Martinelli G;Remondini D;Castellani G
在癌症样品的全外显子组测序数据中检测体细胞突变已经成为分析癌症发展、进展和化疗抗性的流行方法。一些研究提出了软件包、过滤器和参数化。然而，许多研究小组报告说，不同方法之间的一致性很低。我们的目标是开发一种管道，以高验证率检测广泛的单核苷酸突变。我们结合了两种标准工具-基因组分析工具包（GATK）和MuTect -来创建GATK-LODN方法。作为原理的证明，我们将我们的管道应用于血液学（急性骨髓性和急性淋巴细胞白血病）和实体（胃肠道间质瘤和肺腺癌）肿瘤的外显子组测序数据。我们对模拟数据进行了实验，以测试我们的管道的灵敏度和特异性。软件MuTect呈现突变检测的最高验证率（90%），但检测到的体细胞突变数量有限。GATK检测到大量突变，但特异性低。GATK-LODN提高了GATK变体检测的性能（从14个中的5个到4个确认的变体中的3个），同时保留了MuTect未检测到的突变。然而，GATK-LODN在血液样品中比在实体瘤中过滤更多的变体。模拟数据实验表明，GATK-LODN提高了GATK结果的特异性和敏感性。我们提出了一个管道，检测范围广泛的体细胞单核苷酸变异，具有良好的验证率，从癌症样本的外显子组测序数据。我们还展示了结合标准算法创建GATK-LODN方法的优势，可以提高GATK结果的特异性和灵敏度。这条管道可以帮助发现研究，旨在描绘癌症基因组的体细胞突变景观。本文的在线版本（doi：10.1186/s12859-016-1190-7）包含补充材料，可供授权用户使用。
Detecting somatic mutations in whole exome sequencing data of cancer samples has become a popular approach for profiling cancer development, progression and chemotherapy resistance. Several studies have proposed software packages, filters and parametrizations. However, many research groups reported low concordance among different methods. We aimed to develop a pipeline which detects a wide range of single nucleotide mutations with high validation rates. We combined two standard tools – Genome Analysis Toolkit (GATK) and MuTect – to create the GATK-LODN method. As proof of principle, we applied our pipeline to exome sequencing data of hematological (Acute Myeloid and Acute Lymphoblastic Leukemias) and solid (Gastrointestinal Stromal Tumor and Lung Adenocarcinoma) tumors. We performed experiments on simulated data to test the sensitivity and specificity of our pipeline. The software MuTect presented the highest validation rate (90 %) for mutation detection, but limited number of somatic mutations detected. The GATK detected a high number of mutations but with low specificity. The GATK-LODN increased the performance of the GATK variant detection (from 5 of 14 to 3 of 4 confirmed variants), while preserving mutations not detected by MuTect. However, GATK-LODN filtered more variants in the hematological samples than in the solid tumors. Experiments in simulated data demonstrated that GATK-LODN increased both specificity and sensitivity of GATK results. We presented a pipeline that detects a wide range of somatic single nucleotide variants, with good validation rates, from exome sequencing data of cancer samples. We also showed the advantage of combining standard algorithms to create the GATK-LODN method, that increased specificity and sensitivity of GATK results. This pipeline can be helpful in discovery studies aimed to profile the somatic mutational landscape of cancer genomes. The online version of this article (doi:10.1186/s12859-016-1190-7) contains supplementary material, which is available to authorized users.
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影响因子：
9.5
作者：
Pabinger S;Dander A;Fischer M;Snajder R;Sperk M;Efremova M;Krabichler B;Speicher MR;Zschocke J;Trajanoski Z
通讯作者：
Trajanoski Z
影响因子：
14.9
作者：
Nakamura K;Oshima T;Morimoto T;Ikeda S;Yoshikawa H;Shiwa Y;Ishikawa S;Linak MC;Hirai A;Takahashi H;Altaf-Ul-Amin M;Ogasawara N;Kanaya S
通讯作者：
Kanaya S
DOI：
10.1093/bioinformatics/btt375
发表时间：
2013-09-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Roberts ND;Kortschak RD;Parker WT;Schreiber AW;Branford S;Scott HS;Glonek G;Adelson DL
通讯作者：
Adelson DL
影响因子：
20.3
作者：
Bodini, Margherita;Ronchini, Chiara;Riva, Laura
通讯作者：
Riva, Laura
影响因子：
1.9
作者：
Hansen, Marcus Celik;Nederby, Line;Hokland, Peter
通讯作者：
Hokland, Peter