Heber Kerim, Tian Shuchang, Betancurt-Anzola Daniela, Koo Heejung, Bisanz Jordan E
Department of Biochemistry and Molecular Biology, Pennsylvania State University, University Park, PA 16802, USA.
One Health Microbiome Center, Huck Life Sciences Institute, University Park, PA 16802, USA.
bioRxiv. 2024 Aug 9:2024.08.08.607172. doi: 10.1101/2024.08.08.607172.
Synthetic microbial communities offer an opportunity to conduct reductionist research in tractable model systems. However, deriving abundances of highly related strains within these communities is currently unreliable. 16S rRNA gene sequencing does not resolve abundance at the strain level, standard methods for analysis of shotgun metagenomic sequencing do not account for ambiguous mapping between closely related strains, and other methods such as quantitative PCR (qPCR) scale poorly and are resource prohibitive for complex communities. We present StrainR2, which utilizes shotgun metagenomic sequencing paired with a k-mer-based normalization strategy to provide high accuracy strain-level abundances for all members of a synthetic community, provided their genomes.
Both , and using sequencing data derived from gnotobiotic mice colonized with a synthetic fecal microbiota, StrainR2 resolves strain abundances with greater accuracy than other tools utilizing shotgun metagenomic sequencing reads and can resolve complex mixtures of highly related strains. Through experimental validation and benchmarking, we demonstrate that StrainR2's accuracy is comparable to that of qPCR on a subset of strains resolved using absolute quantification. Further, it is capable of scaling to communities of hundreds of strains and efficiently utilizes memory being capable of running both on personal computers and high-performance computing nodes.
Using shotgun metagenomic sequencing reads is a viable method for determining accurate strain-level abundances in synthetic communities using StrainR2.
合成微生物群落为在易于处理的模型系统中进行简化研究提供了机会。然而，目前在这些群落中推导高度相关菌株的丰度是不可靠的。16S rRNA基因测序无法在菌株水平上解析丰度，鸟枪法宏基因组测序的标准分析方法没有考虑密切相关菌株之间的模糊映射，而其他方法如定量PCR（qPCR）扩展性较差，对于复杂群落来说资源消耗过大。我们提出了StrainR2，它利用鸟枪法宏基因组测序并结合基于k-mer的归一化策略，为合成群落的所有成员提供高精度的菌株水平丰度，前提是已知它们的基因组。
无论是使用来自定殖有合成粪便微生物群的无菌小鼠的测序数据，还是使用其他数据，StrainR2解析菌株丰度的准确性都高于其他利用鸟枪法宏基因组测序读数的工具，并且能够解析高度相关菌株的复杂混合物。通过实验验证和基准测试，我们证明StrainR2在使用绝对定量解析的一部分菌株上的准确性与qPCR相当。此外，它能够扩展到包含数百个菌株的群落，并且能够高效利用内存，既可以在个人计算机上运行，也可以在高性能计算节点上运行。
使用鸟枪法宏基因组测序读数是一种可行的方法，可利用StrainR2在合成群落中确定准确的菌株水平丰度。
