Large scale comparison of non-human sequences in human sequencing data.
Large scale comparison of non-human sequences in human sequencing data.
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DOI：
10.1016/j.ygeno.2014.08.009
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发表时间：
2014-12
期刊：
影响因子：
4.4
通讯作者：
Garner, Harold R.
中科院分区：
文献类型：
--
作者：
Tae, Hongseok;Karunasena, Enusha;Bavarva, Jasmin H.;McIver, Lauren J.;Garner, Harold R.
几项研究表明，下一代测序数据中的未映射读段可用于识别感染因子或结构变异，但尚未对单个大型数据集中发现的所有非人类序列进行深入分析和分类。为了确定传染性病原体和假定的污染事件在非人类序列中的共性，我们分析了来自1000基因组计划的150个基因组测序数据文件中的非人类序列，发现平均0.13%的reads与非人类基因组具有相似性。我们比较了基于种族、测序中心和富集方法（全基因组测序与外显子组测序）划分的不同样本组的结果，发现测序中心具有污染基因组的特定签名作为“时间戳”。我们还观察到许多未映射的读数错误地表明污染，因为人类序列与非人类基因组（如小鼠和烟草）中的序列高度相似。
Several studies have demonstrated that unmapped reads in next generation sequencing data could be used to identify infectious agents or structural variants, but there has been no intensive effort to analyze and classify all non-human sequences found in individual large data sets. To identify commonality in non-human sequences by infectious agents and putative contamination events, we analyzed non-human sequences in 150 genomic sequencing data files from the 1000 Genomes Project and observed that 0.13% of reads on average showed similarities to non-human genomes. We compared results among different sample groups divided based on ethnicities, sequencing centers and enrichment methods (whole genome sequencing vs. exome sequencing) and found that sequencing centers had specific signatures of contaminating genomes as ‘time stamps’. We also observed many unmapped reads that falsely indicated contamination because of the high similarity of human sequences to sequences in non-human genome assemblies such as mouse and Nicotiana.
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影响因子：
64.8
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