High-throughput amplicon sequencing of marker genes, such as the 16S rRNA gene in Bacteria and Archaea, provides a wealth of information about the composition of microbial communities. To quantify differences between samples and draw conclusions about factors affecting community assembly, dissimilarity indices are typically used. However, results are subject to several biases, and data interpretation can be challenging. The Jaccard and Bray-Curtis indices, which are often used to quantify taxonomic dissimilarity, are not necessarily the most logical choices. Instead, we argue that Hill-based indices, which make it possible to systematically investigate the impact of relative abundance on dissimilarity, should be used for robust analysis of data. In combination with a null model, mechanisms of microbial community assembly can be analyzed. Here, we also introduce a new software, qdiv, which enables rapid calculations of Hill-based dissimilarity indices in combination with null models. Using amplicon sequencing data from two experimental systems, aerobic granular sludge (AGS) reactors and microbial fuel cells (MFC), we show that the choice of dissimilarity index can have considerable impact on results and conclusions. High dissimilarity between replicates because of random sampling effects make incidence-based indices less suited for identifying differences between groups of samples. Determining a consensus table based on count tables generated with different bioinformatic pipelines reduced the number of low-abundant, potentially spurious amplicon sequence variants (ASVs) in the data sets, which led to lower dissimilarity between replicates. Analysis with a combination of Hill-based indices and a null model allowed us to show that different ecological mechanisms acted on different fractions of the microbial communities in the experimental systems. Hill-based indices provide a rational framework for analysis of dissimilarity between microbial community samples. In combination with a null model, the effects of deterministic and stochastic community assembly factors on taxa of different relative abundances can be systematically investigated. Calculations of Hill-based dissimilarity indices in combination with a null model can be done in qdiv, which is freely available as a Python package ( https://github.com/omvatten/qdiv ). In qdiv, a consensus table can also be determined from several count tables generated with different bioinformatic pipelines.

中文翻译：

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用于分析微生物群落组装的基于山的差异指数和零模型。


标记基因（例如细菌和古细菌中的 16S rRNA 基因）的高通量扩增子测序提供了有关微生物群落组成的丰富信息。为了量化样本之间的差异并得出有关影响群落组装的因素的结论，通常使用相异指数。然而，结果可能会存在一些偏差，并且数据解释可能具有挑战性。杰卡德指数和布雷柯蒂斯指数通常用于量化分类差异，但不一定是最合乎逻辑的选择。相反，我们认为基于希尔的指数可以系统地研究相对丰度对差异性的影响，应该用于数据的稳健分析。结合零模型，可以分析微生物群落组装的机制。在这里，我们还介绍了一种新软件 qdiv，它可以结合零模型快速计算基于 Hill 的相异指数。使用好氧颗粒污泥（AGS）反应器和微生物燃料电池（MFC）这两个实验系统的扩增子测序数据，我们表明相异指数的选择会对结果和结论产生相当大的影响。由于随机抽样效应，重复之间的高度差异使得基于发生率的指数不太适合识别样本组之间的差异。根据不同生物信息学流程生成的计数表确定共识表，减少了数据集中低丰度、潜在虚假扩增子序列变体 (ASV) 的数量，从而降低了重复之间的差异。 通过结合希尔指数和零模型进行分析，我们能够表明不同的生态机制作用于实验系统中微生物群落的不同部分。基于希尔的指数为分析微生物群落样本之间的差异提供了合理的框架。结合零模型，可以系统地研究确定性和随机群落组装因素对不同相对丰度的类群的影响。基于 Hill 的相异指数与空模型的结合可以在 qdiv 中完成，qdiv 可以作为 Python 包免费提供 ( https://github.com/omvatten/qdiv )。在qdiv中，还可以从使用不同生物信息学管道生成的多个计数表中确定共识表。