Diversity analysis and taxonomic profiles can be generated from marker-gene sequence data with the help of many available computational tools.The Quantitative Insights into Microbial Ecology Version 2(QIIME2)has been ...Diversity analysis and taxonomic profiles can be generated from marker-gene sequence data with the help of many available computational tools.The Quantitative Insights into Microbial Ecology Version 2(QIIME2)has been widely used for 16S rRNA data analysis.While many articles have demonstrated the use of QIIME2 with suitable datasets,the application to preclinical data has rarely been talked about.The issues involved in the pre-clinical data include the low-quality score and small sample size that should be addressed properly during analysis.In addition,there are few articles that discuss the detailed statistical methods behind those alpha and beta diversity significance tests that researchers are eager to find.Running the program without knowing the logic behind it is extremely risky.In this article,we first provide a guideline for analyzing 16S rRNA data using QIIME2.Then we will talk about issues in pre-clinical data,and how they could impact the outcome.Finally,we provide brief explanations of statistical methods such as group significance tests and sample size calculation.展开更多
基金S.N.Rai was partly supported with Wendell Cherry Chair in Clinical Trial Research Fund and NIH grants 5P20GM113226(CJM),1P42ES023716(PI:Sanjay Srivastava)and 1P20GM125504(PI:Richard Lamont).C.Qian was supported by the National Institutes of Health grant 5P50AA024337(CJM)and the University of Louisville Fellowship.
文摘Diversity analysis and taxonomic profiles can be generated from marker-gene sequence data with the help of many available computational tools.The Quantitative Insights into Microbial Ecology Version 2(QIIME2)has been widely used for 16S rRNA data analysis.While many articles have demonstrated the use of QIIME2 with suitable datasets,the application to preclinical data has rarely been talked about.The issues involved in the pre-clinical data include the low-quality score and small sample size that should be addressed properly during analysis.In addition,there are few articles that discuss the detailed statistical methods behind those alpha and beta diversity significance tests that researchers are eager to find.Running the program without knowing the logic behind it is extremely risky.In this article,we first provide a guideline for analyzing 16S rRNA data using QIIME2.Then we will talk about issues in pre-clinical data,and how they could impact the outcome.Finally,we provide brief explanations of statistical methods such as group significance tests and sample size calculation.
