Sinha Rashmi, Abu-Ali Galeb, Vogtmann Emily, Fodor Anthony A, Ren Boyu, Amir Amnon, Schwager Emma, Crabtree Jonathan, Ma Siyuan, Abnet Christian C, Knight Rob, White Owen, Huttenhower Curtis
Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland, USA.
Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.
Nat Biotechnol. 2017 Nov;35(11):1077-1086. doi: 10.1038/nbt.3981. Epub 2017 Oct 2.
In order for human microbiome studies to translate into actionable outcomes for health, meta-analysis of reproducible data from population-scale cohorts is needed. Achieving sufficient reproducibility in microbiome research has proven challenging. We report a baseline investigation of variability in taxonomic profiling for the Microbiome Quality Control (MBQC) project baseline study (MBQC-base). Blinded specimen sets from human stool, chemostats, and artificial microbial communities were sequenced by 15 laboratories and analyzed using nine bioinformatics protocols. Variability depended most on biospecimen type and origin, followed by DNA extraction, sample handling environment, and bioinformatics. Analysis of artificial community specimens revealed differences in extraction efficiency and bioinformatic classification. These results may guide researchers in experimental design choices for gut microbiome studies.
为了使人类微生物组研究转化为对健康切实可行的成果,需要对来自人群规模队列的可重复数据进行荟萃分析。事实证明,在微生物组研究中实现足够的可重复性具有挑战性。我们报告了微生物组质量控制(MBQC)项目基线研究(MBQC-base)中分类学分析变异性的基线调查。来自人类粪便、恒化器和人工微生物群落的盲法样本集由15个实验室进行测序,并使用9种生物信息学协议进行分析。变异性主要取决于生物样本类型和来源,其次是DNA提取、样本处理环境和生物信息学。对人工群落样本的分析揭示了提取效率和生物信息学分类的差异。这些结果可能会指导研究人员在肠道微生物组研究的实验设计选择。