2022年6月30日，《自然—方法学》杂志在线发表了美国莱斯大学Todd J. Treangen等研究人员的最新合作成果。该研究开发出一种新方法，可实现全长16S rRNA牛津纳米孔测序数据的物种级微生物群落分析。

研究人员提出了Emu，一种使用期望最大化算法从全长16S rRNA读数中产生分类丰度剖面的方法。从模拟数据集和模拟群落产生的结果显示，Emu能够准确地进行微生物群落分析，同时比其他方法获得更少的假阳性和假阴性。此外，通过比较一个既定的全基因组猎枪测序工作流程产生的临床样本组成估计值，与用Emu处理的全长16S rRNA基因序列产生的估计值，研究人员说明了Emu在现实世界的应用。

据介绍，基于16S核糖体RNA的分析是阐释微生物群落组成的既定标准。虽然短读16S rRNA分析在很大程度上局限于属级的分辨率，因为只有部分基因被测序，但全长16S rRNA基因扩增子序列有可能提供物种级的准确性。然而，现有的分类鉴定算法没有针对长读数据中经常观察到的读长增加和错误率进行优化。

附：英文原文

Title: Emu: species-level microbial community profiling of full-length 16S rRNA Oxford Nanopore sequencing data

Author: 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.

Issue&Volume: 2022-06-30

Abstract: 16S ribosomal RNA-based analysis is the established standard for elucidating the composition of microbial communities. While short-read 16S rRNA analyses are largely confined to genus-level resolution at best, given that only a portion of the gene is sequenced, full-length 16S rRNA gene amplicon 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, an approach that uses an expectation–maximization algorithm to generate taxonomic abundance profiles from full-length 16S rRNA reads. Results produced from simulated datasets and mock communities show that Emu is 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 Emu by comparing clinical sample composition estimates generated by an established whole-genome shotgun sequencing workflow with those returned by full-length 16S rRNA gene sequences processed with Emu.

DOI: 10.1038/s41592-022-01520-4

Source: https://www.nature.com/articles/s41592-022-01520-4