污染
基因分型
外显子组测序
外显子组
全基因组测序
基因组
DNA测序
计算生物学
样品(材料)
参考基因组
公制(单位)
计算机科学
生物
基因型
DNA
遗传学
基因
工程类
生态学
突变
色谱法
化学
运营管理
作者
Wei Lü,Laura D. Gauthier,Timothy Poterba,Edoardo Giacopuzzi,Julia K. Goodrich,Christine Stevens,Daniel King,Mark J. Daly,Benjamin M. Neale,Konrad J. Karczewski
标识
DOI:10.1101/2023.06.28.545801
摘要
DNA sample contamination is a major issue in clinical and research applications of whole genome and exome sequencing. Even modest levels of contamination can substantially affect the overall quality of variant calls and lead to widespread genotyping errors. Currently, popular tools for estimating the contamination level use short-read data (BAM/CRAM files), which are expensive to store and manipulate and often not retained or shared widely. We propose a new metric to estimate DNA sample contamination from variant-level whole genome and exome sequence data, CHARR, Contamination from Homozygous Alternate Reference Reads, which leverages the infiltration of reference reads within homozygous alternate variant calls. CHARR uses a small proportion of variant-level genotype information and thus can be computed from single-sample gVCFs or callsets in VCF or BCF formats, as well as efficiently stored variant calls in Hail VDS format. Our results demonstrate that CHARR accurately recapitulates results from existing tools with substantially reduced costs, improving the accuracy and efficiency of downstream analyses of ultra-large whole genome and exome sequencing datasets.
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