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使用人类全外显子组测序和模拟数据评估变异调用管道的性能。

Performance assessment of variant calling pipelines using human whole exome sequencing and simulated data.

机构信息

Department of Bioinformatics, Aravind Medical Research Foundation, Madurai, Tamil Nadu, 625020, India.

School of Chemical and Biotechnology, SASTRA (Deemed to be University), Thanjavur, Tamil Nadu, 613401, India.

出版信息

BMC Bioinformatics. 2019 Jun 17;20(1):342. doi: 10.1186/s12859-019-2928-9.


DOI:10.1186/s12859-019-2928-9
PMID:31208315
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6580603/
Abstract

BACKGROUND: Whole exome sequencing (WES) is a cost-effective method that identifies clinical variants but it demands accurate variant caller tools. Currently available tools have variable accuracy in predicting specific clinical variants. But it may be possible to find the best combination of aligner-variant caller tools for detecting accurate single nucleotide variants (SNVs) and small insertion and deletion (InDels) separately. Moreover, many important aspects of InDel detection are overlooked while comparing the performance of tools, particularly its base pair length. RESULTS: We assessed the performance of variant calling pipelines using the combinations of four variant callers and five aligners on human NA12878 and simulated exome data. We used high confidence variant calls from Genome in a Bottle (GiaB) consortium for validation, and GRCh37 and GRCh38 as the human reference genome. Based on the performance metrics, both BWA and Novoalign aligners performed better with DeepVariant and SAMtools callers for detecting SNVs, and with DeepVariant and GATK for InDels. Furthermore, we obtained similar results on human NA24385 and NA24631 exome data from GiaB. CONCLUSION: In this study, DeepVariant with BWA and Novoalign performed best for detecting accurate SNVs and InDels. The accuracy of variant calling was improved by merging the top performing pipelines. The results of our study provide useful recommendations for analysis of WES data in clinical genomics.

摘要

背景:全外显子组测序(WES)是一种经济有效的方法,可以识别临床变异,但需要准确的变异呼叫工具。目前可用的工具在预测特定临床变异方面的准确性各不相同。但是,有可能找到最佳的对齐器-变异呼叫器工具组合,分别用于检测准确的单核苷酸变异(SNV)和小插入和缺失(InDel)。此外,在比较工具的性能时,许多重要的 InDel 检测方面被忽视了,特别是其碱基对长度。

结果:我们使用四个变异呼叫器和五个对齐器的组合,对人类 NA12878 和模拟外显子数据评估了变异呼叫管道的性能。我们使用基因组瓶(GiaB)联盟的高置信度变异呼叫进行验证,并将 GRCh37 和 GRCh38 作为人类参考基因组。根据性能指标,BWA 和 Novoalign 对齐器与 DeepVariant 和 SAMtools 呼叫器一起用于检测 SNV 时性能更好,而与 DeepVariant 和 GATK 一起用于检测 InDel 时性能更好。此外,我们在 GiaB 的人类 NA24385 和 NA24631 外显子数据中获得了类似的结果。

结论:在这项研究中,DeepVariant 与 BWA 和 Novoalign 一起用于检测准确的 SNV 和 InDel 效果最佳。通过合并表现最佳的管道,提高了变异呼叫的准确性。我们的研究结果为临床基因组学中 WES 数据的分析提供了有用的建议。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/609f/6580603/b515290b700f/12859_2019_2928_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/609f/6580603/ea1cc8e507d9/12859_2019_2928_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/609f/6580603/c966b1fc182d/12859_2019_2928_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/609f/6580603/2e94f2b93206/12859_2019_2928_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/609f/6580603/bc6d43297fda/12859_2019_2928_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/609f/6580603/10790d22c10b/12859_2019_2928_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/609f/6580603/5df6a66e1f0a/12859_2019_2928_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/609f/6580603/125549a12e14/12859_2019_2928_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/609f/6580603/b515290b700f/12859_2019_2928_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/609f/6580603/ea1cc8e507d9/12859_2019_2928_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/609f/6580603/c966b1fc182d/12859_2019_2928_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/609f/6580603/2e94f2b93206/12859_2019_2928_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/609f/6580603/bc6d43297fda/12859_2019_2928_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/609f/6580603/10790d22c10b/12859_2019_2928_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/609f/6580603/5df6a66e1f0a/12859_2019_2928_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/609f/6580603/125549a12e14/12859_2019_2928_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/609f/6580603/b515290b700f/12859_2019_2928_Fig8_HTML.jpg

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本文引用的文献

[1]
A study on fast calling variants from next-generation sequencing data using decision tree.

BMC Bioinformatics. 2018-4-19

[2]
Optimized detection of insertions/deletions (INDELs) in whole-exome sequencing data.

PLoS One. 2017-8-9

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Genomics. 2017-3

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Nat Rev Genet. 2016-8

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Systematic comparison of variant calling pipelines using gold standard personal exome variants.

Sci Rep. 2015-12-7

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A Comparison of Variant Calling Pipelines Using Genome in a Bottle as a Reference.

Biomed Res Int. 2015

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Hum Genomics. 2015-8-19

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Nat Commun. 2015-2-25

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