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中华临床实验室管理电子杂志 ›› 2020, Vol. 08 ›› Issue (01) : 18 -25. doi: 10.3877/cma.j.issn.2095-5820.2020.01.004

所属专题: 文献

实验研究

三种人全外显子组捕获探针的性能比较
刘菲菲1, 孙明明2, 胡昌明3, 欧小华3, 赵薇薇1,()   
  1. 1. 510005 广州,广州金域医学检验中心有限公司 临床基因组检测中心;510005 广州,广州金域医学检验集团股份有限公司
    3. 510005 广州,广州金域医学检验中心有限公司 临床基因组检测中心
  • 收稿日期:2019-09-20 出版日期:2020-02-28
  • 通信作者: 赵薇薇
  • 基金资助:
    广州市科技计划项目(201604046001); 广州市科技计划项目(201802020030)

Performance comparison of three human exome capture systems

Feifei Liu1, Mingming Sun2, Changming Hu3, Xiaohua Ou3, Weiwei Zhao1()   

  1. 1. Clinical Genome Center, Kingmed Center for Clinical Laboratory, Guangzhou 510005, China; Guangzhou Kingmed Diagnostics Group, Guangzhou 510005, China
    2. Clinical Genome Center, Kingmed Center for Clinical Laboratory, Guangzhou 510005, China; Guangzhou Kingmed Diagnostics Group, Guangzhou 510005, China; Clinical Genome Center, Kingmed Center for Clinical Laboratory, Guangzhou 510005, China
    3. Clinical Genome Center, Kingmed Center for Clinical Laboratory, Guangzhou 510005, China
  • Received:2019-09-20 Published:2020-02-28
  • Corresponding author: Weiwei Zhao
引用本文:

刘菲菲, 孙明明, 胡昌明, 欧小华, 赵薇薇. 三种人全外显子组捕获探针的性能比较[J/OL]. 中华临床实验室管理电子杂志, 2020, 08(01): 18-25.

Feifei Liu, Mingming Sun, Changming Hu, Xiaohua Ou, Weiwei Zhao. Performance comparison of three human exome capture systems[J/OL]. Chinese Journal of Clinical Laboratory Management(Electronic Edition), 2020, 08(01): 18-25.

目的

比较不同平台3个全外显子组探针的性能,为以后建立标准流程提供数据支撑。

方法

提取人EDTA全血或FFPE组织gDNA,通过酶切方法将gDNA片段化,并为每一个样本的片段化后的DNA加上特异序列的接头,利用IDT、Human Exome和MedExomed三种不同的液相探针的方法将目标基因捕获出来,用Illumina的NextSeq 500二代测序仪进行测序;分析测序数据,比较不同捕获探针的靶向覆盖率、捕获特异性、变异检出能力等。

结果

本研究所选入组的3种探针,IDT和MedExome展示了对CCDS和Refseq数据库很好的覆盖率(均>95%);都表现了对靶区域很高的覆盖率(均>99%);IDT探针对血样gDNA捕获特异性(76.96%±0.75%,n=6),明显高于Human Exome (67.63%±1.62%,n=6) (P<0.001);IDT对FFPE标准品捕获特异性为84.48%±0.64%(n=3),明显高于MedExome的71.07%±0.91%(n=3)(P<0.01)。变异输出数量上,Human Exome最高。

结论

IDT探针的捕获特异性明显高于其它2种,而在变异输出数量上,Human Exome探针最高。

Objective

To establish the standard operating procedure for whole exome sequencing (WES), we present a comparison of three probes of different commercial exome capture platforms.

Methods

Genome DNA were extracted from human blood or FFPE slices, then fragmentation by enzyme. Fragments from each sample were ligated with dual-indexed adapters, and target genes were captured by liquid phase probe. Target genes were sequencing by NextSeq 500, and raw data was analyzed by bioinformatics for target region coverage, capture specificity and variations detection.

Results

In our investigation, IDT and MedExome showed great coverage of CCDS and Refseq. All the three systems showed great coverage of target region. IDT showed the best capture specificity. Human Exome showed the best capability of variations detection.

Conclusion

Each of the three human exome capture probes have their advantages.

图1 WES整体流程图
表1 检测主要试剂
表2 主要仪器清单
图2 目的片段杂交捕获流程示意图
图3 不同探针覆盖区域的比较示意图
表3 不同探针与数据库比较结果
图4 IDT与Roche/Nim捕获探针特异性比较
表4 IDT与Roche/Nim捕获探针特异性比较
图5 IDT与Roche/Nim对靶区域覆盖率比较
图6 IDT与Roche/Nim捕获探针变异检出能力比较
表5 IDT与Roche/Nim捕获探针变异检出能力比较(单位:个/Mb)
3
Sarah B Ng, Kati J Buckingham, Choli Lee, et al. Exome sequening identifies the cause of a mendelian disorder[J]. Nature genetics,2010,42(1):30-35.
4
Wang X, Wang H, Cao M, et al. Whole-exome sequencing identifies ALMS1,IQCB1, CNGA3, and MYO7A mutations in patients with Leber congenital amaurosis[J]. Hum Mutat, 2011,32(12):1450-1459.
5
Wang H, Chen X, Dudinsky L, et al. Exome capture sequencing identifies a novel mutation in BBS4[J]. Mol Vis, 2011,17:3529-3540.
6
Flannick J, Beer NL, Bick AG, et al. Assessing the phenotypic effects in the general population of rare variants in genes for a dominant Mendelian form of diabetes[J]. Nat Genet, 2013,45(11):1380-1385.
7
Xu Y, Xiao B, Jiang WT, et al. A novel mutation identified in PKHD1 by targeted exome sequencing: guiding prenatal diagnosis for an ARPKD family[J]. Gene, 2014,551(1):33-38.
8
Smith JD, Hing AV, Clarke CM, et al. Exome sequencing identifies a recurrent de novo ZSWIM6 mutation associated with acromelic frontonasal dysostosis[J]. Am J Hum Genet, 2014,95(2):235-240.
9
Yang Y, Muzny DM, Reid JG, et al.Clinical whole-exome sequencing for the diagnosis of mendelian disorders[J]. N Engl J Med, 2013,369(16): 1502-1511.
10
Lee H, Deignan JL, Dorrani N, et al. Clinical exome sequencing for genetic identification of rare Mendelian disorders[J].JAMA, 2014,312(18):1880-1887.
11
Soden SE, Saunders CJ, Willig LK, et al. Effectiveness of exome and genome sequencing guided by acuity of illness for diagnosis of neurodevelopmental disorders[J]. Sci Transl Med, 2014,6(265):265ra168.
12
Yang Y, Muzny DM, Xia F, et al.Molecular findings among patients referred for clinical whole-exome sequencing[J]. JAMA, 2014,312(18):1870-1879.
13
Santani A, Murrell J, Funke B, et al. Development and Validation of Targeted Next-Generation Sequencing Panels for Detection of Germline Variants in Inherited Diseases[J]. Arch Pathol Lab Med, 2017,141(6):787-797.
14
Head SR, Komori HK, LaMere SA, et al. Library construction for next-generation sequencing: overviews and challenges[J]. Biotechniques, 2014,56(2):61-64,66,68.
15
Quail MA, Swerdlow H, Turner DJ. Improved protocols for the illumina genome analyzer sequencing system[J]. Curr Protoc Hum Genet, 2009,Chapter 18:Unit 18.2.
16
Jennings LJ, Arcila ME, Corless C, et al. Guidelines for Validation of Next-Generation Sequencing-Based Oncology Panels: A Joint Consensus Recommendation of the Association for Molecular Pathology and College of American Pathologists[J]. J Mol Diagn, 2017,19(3):341-365.
17
Clark MJ, Chen R, Lam HY, et al. Performance comparison of exome DNA sequencing technologies[J]. Nat Biotechnol, 2011,29(10): 908-914.
18
Li H, Durbin R. Fast and accurate long-read alignment with Burrows-Wheeler transform[J]. Bioinformatics, 2010,26(5):589-595.
19
Li H, Handsaker B, Wysoker A, et al. The Sequence Alignment/Map format and SAMtools[J]. Bioinformatics, 2009,25(16):2078-2079.
20
Koboldt DC, Zhang Q, Larson DE, et al. VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing[J]. Genome Res, 2012,22(3):568-576.
21
McKenna A, Hanna M, Banks E, et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data[J]. Genome Res, 2010,20(9):1297-1303.
22
Wang K, Li M, Hakonarson H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data[J]. Nucleic Acids Res, 2010,38(16):e164.
23
Ruark E, Renwick A, Clarke M, et al. The ICR142 NGS validation series: a resource for orthogonal assessment of NGS analysis[J].F1000Res, 2016,5:386.
24
Hintzsche JD, Robinson WA, Tan AC. A Survey of Computational Tools to Analyze and Interpret Whole Exome Sequencing Data[J]. Int J Genomics, 2016,6:7983236.
25
Dong C, Wei P, Jian X, et al. Comparison and integration of deleteriousness prediction methods for nonsynonymous SNVs in whole exome sequencing studies[J]. Hum Mol Genet, 2015,24(8):2125-2137.
26
Clark MJ, Chen R, Lam HY, et al. Performance comparison of exome DNA sequencing technologies[J]. Nat Biotechnol, 2011,29(10):908-914.
27
Chilamakuri CS, Lorenz S, Madoui MA, et al. Performance comparison of four exome capture systems for deep sequencing[J]. BMC Genomics, 2014,15:449.
28
Green B, Bouchier C, Fairhead C, et al. Insertion site preference of Mu,Tn5,and Tn7 transposons[J]. Mob DNA, 2012,3(1):3.
29
Shigemizu D, Momozawa Y, Abe T, et al. Performance comparison of four commercial human whole-exome capture platforms[J]. Sci Rep, 2015,5:12742.
30
Bonfiglio S, Vanni I, Rossella V, et al. Performance comparison of two commercial human whole-exome capture systems on formalin-fixed paraffin-embedded lung adenocarcinoma samples[J]. BMC Cancer, 2016,16(1):692.
1
Ng SB, Turner EH, Robertson PD, et al. Targeted capture and massively parallel sequencing of 12 human exomes[J]. Nature 461(7261):272-276.
2
Choi M, Scholl UI, Ji W, et al. Genetic diagnosis by whole exome capture and massively parallel DNA sequencing[J]. Proc Natl Acad Sci USA, 2009,106(45):19096-19101.
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