PDF(Pubmed)
Abstract：
Metagenomic sequencing analysis is central to investigating microbial communities in clinical and environmental studies. Short-read sequencing remains the primary approach for metagenomic research; however, long-read sequencing may offer advantages of improved metagenomic assembly and resolved taxonomic identification. To compare the relative performance for metagenomic studies, we simulated short- and long-read datasets using increasingly complex metagenomes comprising 10, 20, and 50 microbial taxa. Additionally, we used an empirical dataset of paired short- and long-read data generated from mouse fecal pellets to assess real-world performance. We compared metagenomic assembly quality, taxonomic classification, and metagenome-assembled genome (MAG) recovery rates. We show that long-read sequencing data significantly improve taxonomic classification and assembly quality. Metagenomic assemblies using simulated long reads were more complete and more contiguous with higher rates of MAG recovery. This resulted in more precise taxonomic classifications. Principal component analysis of empirical data demonstrated that sequencing technology affects compositional results as samples clustered by sequence type, not sample type. Overall, we highlight strengths of long-read metagenomic sequencing for microbiome studies, including improving the accuracy of classification and relative abundance estimates. These results will aid researchers when considering which sequencing approaches to use for metagenomic projects.
摘要：
宏基因组测序分析是临床和环境研究中调查微生物群落的核心。短读测序仍然是宏基因组研究的主要方法；然而，长读数测序可能提供改善宏基因组组装和解析分类学鉴定的优势.为了比较宏基因组研究的相对表现，我们使用包含10,20和50个微生物类群的日益复杂的宏基因组模拟了短阅读和长阅读数据集.此外,我们使用了由小鼠粪便颗粒产生的配对短读和长读数据的经验数据集来评估真实世界表现.我们比较了宏基因组组装质量，分类学分类，和宏基因组组装的基因组（MAG）回收率。我们表明，长读数测序数据显着提高了分类分类和组装质量。使用模拟长读数的宏基因组组装更完整且更连续,具有更高的MAG回收率。这导致了更精确的分类学分类。经验数据的主成分分析表明，测序技术会影响组成结果，因为样本按序列类型聚类，不是样品类型。总的来说,我们强调了长期阅读宏基因组测序用于微生物组研究的优势，包括提高分类和相对丰度估计的准确性。这些结果将帮助研究人员考虑哪些测序方法用于宏基因组项目。
