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在标记基因数据分析中,精确序列变体应取代操作分类单元。

Exact sequence variants should replace operational taxonomic units in marker-gene data analysis.

作者信息

Callahan Benjamin J, McMurdie Paul J, Holmes Susan P

机构信息

Department of Population Health and Pathobiology, NC State University, Raleigh NC, USA.

Whole Biome Inc, San Francisco CA, USA.

出版信息

ISME J. 2017 Dec;11(12):2639-2643. doi: 10.1038/ismej.2017.119. Epub 2017 Jul 21.

Abstract

Recent advances have made it possible to analyze high-throughput marker-gene sequencing data without resorting to the customary construction of molecular operational taxonomic units (OTUs): clusters of sequencing reads that differ by less than a fixed dissimilarity threshold. New methods control errors sufficiently such that amplicon sequence variants (ASVs) can be resolved exactly, down to the level of single-nucleotide differences over the sequenced gene region. The benefits of finer resolution are immediately apparent, and arguments for ASV methods have focused on their improved resolution. Less obvious, but we believe more important, are the broad benefits that derive from the status of ASVs as consistent labels with intrinsic biological meaning identified independently from a reference database. Here we discuss how these features grant ASVs the combined advantages of closed-reference OTUs-including computational costs that scale linearly with study size, simple merging between independently processed data sets, and forward prediction-and of de novo OTUs-including accurate measurement of diversity and applicability to communities lacking deep coverage in reference databases. We argue that the improvements in reusability, reproducibility and comprehensiveness are sufficiently great that ASVs should replace OTUs as the standard unit of marker-gene analysis and reporting.

摘要

近年来的进展使得无需借助传统的分子操作分类单元(OTU)构建方法来分析高通量标记基因测序数据成为可能:OTU是指测序读数的聚类,其差异小于固定的不相似性阈值。新方法能够充分控制误差,从而可以精确解析扩增子序列变体(ASV),直至测序基因区域内单核苷酸差异的水平。更高分辨率的优势显而易见,支持ASV方法的观点主要集中在其提高的分辨率上。不太明显但我们认为更重要的是,ASV作为与参考数据库独立识别的具有内在生物学意义的一致标签,所带来的广泛益处。在此我们讨论这些特性如何赋予ASV封闭参考OTU的综合优势——包括计算成本与研究规模呈线性关系、独立处理数据集之间的简单合并以及正向预测——以及从头OTU的优势——包括准确测量多样性和适用于参考数据库中缺乏深度覆盖的群落。我们认为,在可重用性、可重复性和全面性方面的改进非常显著,以至于ASV应取代OTU,成为标记基因分析和报告的标准单元。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c909/5702726/eabd8e427520/ismej2017119f1.jpg

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