Bowers Robert M, Clum Alicia, Tice Hope, Lim Joanne, Singh Kanwar, Ciobanu Doina, Ngan Chew Yee, Cheng Jan-Fang, Tringe Susannah G, Woyke Tanja
Microbial Genomics Program Lead, DOE Joint Genome Institute, 2800 Mitchell Dr, Walnut Creek, CA, USA.
BMC Genomics. 2015 Oct 24;16:856. doi: 10.1186/s12864-015-2063-6.
BACKGROUND: The rapid development of sequencing technologies has provided access to environments that were either once thought inhospitable to life altogether or that contain too few cells to be analyzed using genomics approaches. While 16S rRNA gene microbial community sequencing has revolutionized our understanding of community composition and diversity over time and space, it only provides a crude estimate of microbial functional and metabolic potential. Alternatively, shotgun metagenomics allows comprehensive sampling of all genetic material in an environment, without any underlying primer biases. Until recently, one of the major bottlenecks of shotgun metagenomics has been the requirement for large initial DNA template quantities during library preparation. RESULTS: Here, we investigate the effects of varying template concentrations across three low biomass library preparation protocols on their ability to accurately reconstruct a mock microbial community of known composition. We analyze the effects of input DNA quantity and library preparation method on library insert size, GC content, community composition, assembly quality and metagenomic binning. We found that library preparation method and the amount of starting material had significant impacts on the mock community metagenomes. In particular, GC content shifted towards more GC rich sequences at the lower input quantities regardless of library prep method, the number of low quality reads that could not be mapped to the reference genomes increased with decreasing input quantities, and the different library preparation methods had an impact on overall metagenomic community composition. CONCLUSIONS: This benchmark study provides recommendations for library creation of representative and minimally biased metagenome shotgun sequencing, enabling insights into functional attributes of low biomass ecosystem microbial communities.
背景:测序技术的快速发展使得人们能够进入一些曾经被认为完全不适宜生命存在的环境,或者是那些细胞数量过少而无法使用基因组学方法进行分析的环境。虽然16S rRNA基因微生物群落测序彻底改变了我们对群落组成和多样性在时间和空间上的理解,但它只能对微生物的功能和代谢潜力进行粗略估计。相比之下,鸟枪法宏基因组学允许对环境中的所有遗传物质进行全面采样,而不存在任何潜在的引物偏差。直到最近,鸟枪法宏基因组学的一个主要瓶颈一直是文库制备过程中对大量初始DNA模板量的要求。 结果:在这里,我们研究了在三种低生物量文库制备方案中改变模板浓度对其准确重建已知组成的模拟微生物群落能力的影响。我们分析了输入DNA量和文库制备方法对文库插入片段大小、GC含量、群落组成、组装质量和宏基因组分箱的影响。我们发现文库制备方法和起始材料的量对模拟群落宏基因组有显著影响。特别是,无论文库制备方法如何,在较低输入量时GC含量都向富含GC的序列偏移,无法映射到参考基因组的低质量读数数量随着输入量的减少而增加,并且不同的文库制备方法对整体宏基因组群落组成有影响。 结论:这项基准研究为创建具有代表性且偏差最小的宏基因组鸟枪法测序文库提供了建议,有助于深入了解低生物量生态系统微生物群落的功能属性。
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