Emu: species-level microbial community profiling of full-length 16S rRNA Oxford Nanopore sequencing data.
Emu: species-level microbial community profiling of full-length 16S rRNA Oxford Nanopore sequencing data.
复制标题
Emu：全长16S rRNA牛津纳米孔测序数据的物种水平微生物群落分析。
DOI：
10.1038/s41592-022-01520-4
复制
发表时间：
2022-07
期刊：
影响因子：
48
通讯作者：
Treangen, Todd J.
中科院分区：
文献类型：
--
作者：
Curry, Kristen D.;Wang, Qi;Nute, Michael G.;Tyshaieva, Alona;Reeves, Elizabeth;Soriano, Sirena;Wu, Qinglong;Graeber, Enid;Finzer, Patrick;Mendling, Werner;Savidge, Tor;Villapol, Sonia;Dilthey, Alexander;Treangen, Todd J.
基于16S rRNA的分析是阐明微生物群落组成的既定标准。虽然短读16S分析在很大程度上仅限于属水平的分辨率，因为只有一部分基因被测序，但全长16S序列有可能提供物种水平的准确性。然而，现有的分类识别算法没有针对在长时间读取数据中经常观察到的增加的读取长度和错误率进行优化。在这里，我们提出了一种新的方法EMU，它使用了一种期望最大化(EM)算法来从全长的16S rRNA读取中生成分类丰度分布。来自两个模拟数据集和两个模拟群落的结果表明，与其他方法相比，Emu能够准确地描述微生物群落，同时获得更少的假阳性和假阴性。此外，我们通过比较已建立的全基因组鸟枪测序工作流程生成的临床样本成分估计值与EMU处理的全长16S序列返回的临床样本成分估计值，说明了我们新软件的真实应用。
16S rRNA based analysis is the established standard for elucidating microbial community composition. While short read 16S analyses are largely confined to genus-level resolution at best since only a portion of the gene is sequenced, full-length 16S sequences have the potential to provide species-level accuracy. However, existing taxonomic identification algorithms are not optimized for the increased read length and error rate often observed in long-read data. Here we present Emu, a novel approach that employs an expectation-maximization (EM) algorithm to generate taxonomic abundance profiles from full-length 16S rRNA reads. Results produced from two simulated datasets and two mock communities show Emu capable of accurate microbial community profiling while obtaining fewer false positives and false negatives than alternative methods. Additionally, we illustrate a real-world application of our new software by comparing clinical sample composition estimates generated by an established whole-genome shotgun sequencing workflow to those returned by full-length 16S sequences processed with Emu.
登录
查看更多内容
影响因子：
3.7
作者：
Miller CS;Handley KM;Wrighton KC;Frischkorn KR;Thomas BC;Banfield JF
通讯作者：
Banfield JF
影响因子：
4.2
作者：
Brooks JP;Edwards DJ;Harwich MD Jr;Rivera MC;Fettweis JM;Serrano MG;Reris RA;Sheth NU;Huang B;Girerd P;Vaginal Microbiome Consortium;Strauss JF 3rd;Jefferson KK;Buck GA
通讯作者：
Buck GA
影响因子：
14.9
作者：
Clark K;Karsch-Mizrachi I;Lipman DJ;Ostell J;Sayers EW
通讯作者：
Sayers EW
影响因子：
2.7
作者：
Edgar RC
通讯作者：
Edgar RC
影响因子：
48
作者：
Karst, Soren M.;Ziels, Ryan M.;Albertsen, Mads
通讯作者：
Albertsen, Mads