文章：Optimizing methods and dodging pitfalls in microbiome research

时间：2017

杂志：Microbiome

影响metagenome数据产出的因素：

1）抗生素、药物的使用

2）年龄

3）饮食

4）地域

5）宠物

6）动物需要考虑饲养环境（例如小鼠存在吃便便的情况）

7）低起始量（多次的PCR，容易带来污染）

8）样品存储条件

9）DNA提取试剂盒会带来污染（改变了对实验环节的认知，实验过程的试剂盒会带来污染）

10）阴性和阳性对照

11）长期跟踪和validation的实验设计

...

为此针对实验环节的整体建议：

1）数据分析多因素的考虑，For analysis, multiple confounding factors need to be taken into account, including antibiotic use, age, sex, diet, geography, and pet ownership.

2）动物嗜便的考虑，In animal studies, cage effects can dominate over what may seem to be extreme interventions. Thus, it is critical to set up each condition to be studied in multiple cages, so that the caging variable can be isolated and accounted for.

3）样品存放条件，Although we recommend storing samples, especially fecal samples, at −80 °C immediately after collection for most accurate results, alternative storage methods for field studies also lead to results with relatively small deviations. For new sample types, it will be wise to test for changes during storage under study-specific storage conditions.

4）取样是否有代表性，In a cross-sectional study, it is essential to know whether the time point sampled will be representative. For example, the healthy adult gut microbiota does not change radically over short time scales, but that of the vagina sometimes does. Therefore, it is important to assess the relationship of possible longitudinal dynamics to the question posed.

5）对照的设计，Be energetic in creating and analyzing negative controls—DNA extraction kits usually come with contaminants, and contamination may vary between suppliers and even between batches of the same kit.

6）阳性对照，Use positive controls for each batch of samples. Mock communities are valuable for this, and the simple synthetic DNA controls presented here are also quite useful. Place controls asymmetrically in purification plates to verify proper sample tracking through the DNA purification and library preparation procedures.

7）低起始量，Low microbial biomass samples present many challenges. When starting a study that might involve low microbial biomass samples, it is essential to quantify the microbial load in the samples to understand the extent of the challenge. QPCR of total 16S rRNA gene copies can be used for this purpose, as can conventional plating assays if applicable. In an experiment that may involve low biomass samples, start with the null hypothesis that all sequence data reflects contamination only, and ask whether this idea can be rejected in a statistical analysis of the data.

8）数据处理的多重比对，Be realistic about “data dredging,” that is, imposing a rigorous statistical method to control multiple comparisons.

9）单独验证组的设计，Lastly, if affordable, it greatly strengthens a study to assess effects in separate discovery and validation cohorts.