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用于靶向关联研究和下一代序列验证的定制设计Illumina基因型聚类图解读。

Interpretation of custom designed Illumina genotype cluster plots for targeted association studies and next-generation sequence validation.

作者信息

Tindall Elizabeth A, Petersen Desiree C, Nikolaysen Stina, Miller Webb, Schuster Stephan C, Hayes Vanessa M

机构信息

Cancer Genetics Group, Children's Cancer Institute Australia for Medical Research, Sydney Children's Hospital, High Street, Randwick, NSW 2031, Australia.

出版信息

BMC Res Notes. 2010 Feb 22;3:39. doi: 10.1186/1756-0500-3-39.

Abstract

BACKGROUND

High-throughput custom designed genotyping arrays are a valuable resource for biologically focused research studies and increasingly for validation of variation predicted by next-generation sequencing (NGS) technologies. We investigate the Illumina GoldenGate chemistry using custom designed VeraCode and sentrix array matrix (SAM) assays for each of these applications, respectively. We highlight applications for interpretation of Illumina generated genotype cluster plots to maximise data inclusion and reduce genotyping errors.

FINDINGS

We illustrate the dramatic effect of outliers in genotype calling and data interpretation, as well as suggest simple means to avoid genotyping errors. Furthermore we present this platform as a successful method for two-cluster rare or non-autosomal variant calling. The success of high-throughput technologies to accurately call rare variants will become an essential feature for future association studies. Finally, we highlight additional advantages of the Illumina GoldenGate chemistry in generating unusually segregated cluster plots that identify potential NGS generated sequencing error resulting from minimal coverage.

CONCLUSIONS

We demonstrate the importance of visually inspecting genotype cluster plots generated by the Illumina software and issue warnings regarding commonly accepted quality control parameters. In addition to suggesting applications to minimise data exclusion, we propose that the Illumina cluster plots may be helpful in identifying potential in-put sequence errors, particularly important for studies to validate NGS generated variation.

摘要

背景

高通量定制基因分型阵列对于聚焦生物学的研究而言是一种宝贵资源,并且在验证下一代测序(NGS)技术预测的变异方面的应用也越来越多。我们分别针对这些应用,研究了使用定制设计的VeraCode和Sentrix阵列矩阵(SAM)分析的Illumina GoldenGate化学方法。我们重点介绍了Illumina生成的基因型聚类图的解读应用,以最大限度地纳入数据并减少基因分型错误。

研究结果

我们阐述了异常值在基因型判定和数据解读中的显著影响,并提出了避免基因分型错误的简单方法。此外,我们将此平台展示为一种成功的用于双聚类罕见或非常染色体变异判定的方法。高通量技术准确判定罕见变异的成功将成为未来关联研究的一项基本特征。最后,我们强调了Illumina GoldenGate化学方法在生成异常分离的聚类图方面的额外优势,这些聚类图可识别因低覆盖度导致的潜在NGS测序错误。

结论

我们证明了目视检查Illumina软件生成的基因型聚类图的重要性,并就普遍接受的质量控制参数发出警告。除了建议采用可尽量减少数据排除的应用外,我们还提出Illumina聚类图可能有助于识别潜在的输入序列错误,这对于验证NGS产生的变异的研究尤为重要。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7d4a/2848685/512c3115ba05/1756-0500-3-39-1.jpg

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