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Statistical guidelines for quality control of next-generation sequencing techniques.
Life Science Alliance ( IF 4.4 ) Pub Date : 2021-08-30 , DOI: 10.26508/lsa.202101113
Maximilian Sprang 1 , Matteo Krüger 1 , Miguel A Andrade-Navarro 1 , Jean-Fred Fontaine 2
Affiliation  

More and more next-generation sequencing (NGS) data are made available every day. However, the quality of this data is not always guaranteed. Available quality control tools require profound knowledge to correctly interpret the multiplicity of quality features. Moreover, it is usually difficult to know if quality features are relevant in all experimental conditions. Therefore, the NGS community would highly benefit from condition-specific data-driven guidelines derived from many publicly available experiments, which reflect routinely generated NGS data. In this work, we have characterized well-known quality guidelines and related features in big datasets and concluded that they are too limited for assessing the quality of a given NGS file accurately. Therefore, we present new data-driven guidelines derived from the statistical analysis of many public datasets using quality features calculated by common bioinformatics tools. Thanks to this approach, we confirm the high relevance of genome mapping statistics to assess the quality of the data, and we demonstrate the limited scope of some quality features that are not relevant in all conditions. Our guidelines are available at https://cbdm.uni-mainz.de/ngs-guidelines.

中文翻译:

下一代测序技术质量控制的统计指南。

每天都有越来越多的下一代测序 (NGS) 数据可供使用。但是,这些数据的质量并不总是得到保证。可用的质量控制工具需要深厚的知识才能正确解释质量特征的多样性。此外,通常很难知道质量特征是否在所有实验条件下都相关。因此,NGS 社区将从许多公开可用的实验得出的特定条件数据驱动指南中受益匪浅,这些指南反映了常规生成的 NGS 数据。在这项工作中,我们在大数据集中描述了众所周知的质量指南和相关特征,并得出结论,它们对于准确评估给定 NGS 文件的质量来说太有限了。所以,我们提出了新的数据驱动指南,这些指南来自使用常见生物信息学工具计算的质量特征对许多公共数据集进行统计分析。由于这种方法,我们确认了基因组作图统计与评估数据质量的高度相关性,并且我们证明了一些在所有条件下都不相关的质量特征的范围有限。我们的指南可在 https://cbdm.uni-mainz.de/ngs-guidelines 获得。
更新日期:2021-08-30
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