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人类基因组中小变异calls 的基准测试最佳实践。

Best practices for benchmarking germline small-variant calls in human genomes.

机构信息

Illumina Cambridge Ltd, Little Chesterford, UK.

Real Time Genomics, Hamilton, New Zealand.

出版信息

Nat Biotechnol. 2019 May;37(5):555-560. doi: 10.1038/s41587-019-0054-x. Epub 2019 Mar 11.

Abstract

Standardized benchmarking approaches are required to assess the accuracy of variants called from sequence data. Although variant-calling tools and the metrics used to assess their performance continue to improve, important challenges remain. Here, as part of the Global Alliance for Genomics and Health (GA4GH), we present a benchmarking framework for variant calling. We provide guidance on how to match variant calls with different representations, define standard performance metrics, and stratify performance by variant type and genome context. We describe limitations of high-confidence calls and regions that can be used as truth sets (for example, single-nucleotide variant concordance of two methods is 99.7% inside versus 76.5% outside high-confidence regions). Our web-based app enables comparison of variant calls against truth sets to obtain a standardized performance report. Our approach has been piloted in the PrecisionFDA variant-calling challenges to identify the best-in-class variant-calling methods within high-confidence regions. Finally, we recommend a set of best practices for using our tools and evaluating the results.

摘要

需要标准化的基准测试方法来评估从序列数据中调用的变异体的准确性。尽管变异调用工具和用于评估其性能的指标在不断改进,但仍存在重要挑战。在这里,作为全球基因组学和健康联盟(GA4GH)的一部分,我们提出了一个用于变异调用的基准测试框架。我们提供了如何匹配不同表示形式的变异调用、定义标准性能指标以及按变异类型和基因组上下文分层性能的指导。我们描述了高可信度调用的局限性和可用作真实数据集的区域(例如,两种方法的单核苷酸变异一致性在高可信度区域内为 99.7%,而在区域外为 76.5%)。我们的基于网络的应用程序使比较变异调用与真实数据集能够获得标准化的性能报告。我们的方法已在 PrecisionFDA 变异调用挑战中进行了试点,以确定高可信度区域内的最佳变异调用方法。最后,我们建议了一套使用我们的工具和评估结果的最佳实践。

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