Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data.
Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data.
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DOI：
10.1186/s40168-018-0605-2
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发表时间：
2018-12-17
期刊：
影响因子：
15.5
通讯作者：
Callahan, Benjamin J
中科院分区：
文献类型：
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作者：
Davis, Nicole M;Proctor, Diana M;Callahan, Benjamin J
背景技术背景：基于标记基因和宏基因组测序（MGS）的微生物群落调查的准确性受到污染物的存在-DNA序列并不真正存在于样品中。污染物来自各种来源，包括试剂。适当的实验室实践可以减少污染，但不能消除污染。在这里，我们介绍了decontam（https://github.com/benjjneb/decontam），一个开源的R软件包，它实现了一个统计分类程序，根据两个广泛复制的模式识别MGS数据中的污染物：污染物在低浓度样品中出现的频率较高，通常在阴性对照中发现。Decontam分类扩增子序列变体（ASV）在人类口腔数据集中，与先前对栖息在该环境中的微生物类群的显微镜观察和先前对污染物类群的报道一致。在稀释系列的宏基因组学和标记基因测量中，decontam大大减少了由不同测序方案引起的技术差异。decontam的应用程序最近发表的两个数据集证实并扩展了他们的结论，即几乎没有证据存在的土著胎盘微生物组和一些低频类群似乎与早产contaminations.Conclusions相关：Decontam提高了宏基因组和标记基因测序的质量，通过识别和删除污染的DNA序列。Decontam可以轻松地与现有的MGS工作流程集成，使研究人员能够以很少或没有额外成本生成更准确的微生物群落概况。
BACKGROUND: The accuracy of microbial community surveys based on marker-gene and metagenomic sequencing (MGS) suffers from the presence of contaminants-DNA sequences not truly present in the sample. Contaminants come from various sources, including reagents. Appropriate laboratory practices can reduce contamination, but do not eliminate it. Here we introduce decontam ( https://github.com/benjjneb/decontam ), an open-source R package that implements a statistical classification procedure that identifies contaminants in MGS data based on two widely reproduced patterns: contaminants appear at higher frequencies in low-concentration samples and are often found in negative controls.RESULTS: Decontam classified amplicon sequence variants (ASVs) in a human oral dataset consistently with prior microscopic observations of the microbial taxa inhabiting that environment and previous reports of contaminant taxa. In metagenomics and marker-gene measurements of a dilution series, decontam substantially reduced technical variation arising from different sequencing protocols. The application of decontam to two recently published datasets corroborated and extended their conclusions that little evidence existed for an indigenous placenta microbiome and that some low-frequency taxa seemingly associated with preterm birth were contaminants.CONCLUSIONS: Decontam improves the quality of metagenomic and marker-gene sequencing by identifying and removing contaminant DNA sequences. Decontam integrates easily with existing MGS workflows and allows researchers to generate more accurate profiles of microbial communities at little to no additional cost.