From Wet-Lab to Variations: Concordance and Speed of Bioinformatics Pipelines for Whole Genome and Whole Exome Sequencing.
From Wet-Lab to Variations: Concordance and Speed of Bioinformatics Pipelines for Whole Genome and Whole Exome Sequencing.
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DOI：
10.1002/humu.23114
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发表时间：
2016-12
期刊：
影响因子：
3.9
通讯作者：
Beltran, Sergi
中科院分区：
文献类型：
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作者：
Laurie, Steve;Fernandez-Callejo, Marcos;Marco-Sola, Santiago;Trotta, Jean-Remi;Camps, Jordi;Chacon, Alejandro;Espinosa, Antonio;Gut, Marta;Gut, Ivo;Heath, Simon;Beltran, Sergi
关键词：
随着全基因组测序变得更便宜和更快，它将逐步取代有针对性的下一代测序作为研究和诊断的标准实践。然而，计算成本性能比并没有以同等速度前进。因此，考虑到所需的计算资源，评估变体检测过程的鲁棒性是至关重要的。我们已经对来自NA12878人类样本的全基因组和全外显子组测序数据的最新读取比对器（BWA-MEM和GEM3）和变异调用器（FreeBayes、GATK HaplotypeCaller、SAMtools）的六种组合进行了基准测试。结果已经在它们之间进行了比较，并与NIST瓶中基因组（GIAB）变体参考数据集进行了比较。我们报告了在该过程的某些步骤中高达20倍的速度差异，并观察到SNV和较小程度的InDel检测在70%的基因组中高度一致。SNV，特别是InDel，在20%的基因组中检测不太可靠，在剩下的10%中几乎不可行。这些发现将有助于选择适当的工具，同时考虑到目标、工作量和可用的计算基础设施。
As whole genome sequencing becomes cheaper and faster, it will progressively substitute targeted next‐generation sequencing as standard practice in research and diagnostics. However, computing cost–performance ratio is not advancing at an equivalent rate. Therefore, it is essential to evaluate the robustness of the variant detection process taking into account the computing resources required. We have benchmarked six combinations of state‐of‐the‐art read aligners (BWA‐MEM and GEM3) and variant callers (FreeBayes, GATK HaplotypeCaller, SAMtools) on whole genome and whole exome sequencing data from the NA12878 human sample. Results have been compared between them and against the NIST Genome in a Bottle (GIAB) variants reference dataset. We report differences in speed of up to 20 times in some steps of the process and have observed that SNV, and to a lesser extent InDel, detection is highly consistent in 70% of the genome. SNV, and especially InDel, detection is less reliable in 20% of the genome, and almost unfeasible in the remaining 10%. These findings will aid in choosing the appropriate tools bearing in mind objectives, workload, and computing infrastructure available.
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DOI：
10.1093/bioinformatics/btr670
发表时间：
2012-02-01
期刊：
Bioinformatics (Oxford, England)
影响因子：
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作者：
Boeva V;Popova T;Bleakley K;Chiche P;Cappo J;Schleiermacher G;Janoueix-Lerosey I;Delattre O;Barillot E
通讯作者：
Barillot E
影响因子：
3.7
作者：
Derrien T;Estellé J;Marco Sola S;Knowles DG;Raineri E;Guigó R;Ribeca P
通讯作者：
Ribeca P
影响因子：
12.3
作者：
Patwardhan, Anil;Harris, Jason;Chen, Richard
通讯作者：
Chen, Richard
影响因子：
5.8
作者：
Li, Heng
通讯作者：
Li, Heng
使用下一代 DNA 测序数据进行变异发现和基因分型的框架。
影响因子：
30.8
作者：
通讯作者：
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