Environmental microbiome studies rely on fast and accurate bioinformatics tools to characterize the taxonomic composition of samples based on the 16S rRNA gene. MetaGenome Rapid Annotation using Subsystem Technology (MG-RAST) and Quantitative Insights Into Microbial Ecology 2 (QIIME2) are two of the most popular tools available to perform this task. Their underlying algorithms differ in many aspects, and therefore the comparison of the pipelines provides insights into their best use and interpretation of the outcomes. Both of these bioinformatics tools are based on several specialized algorithms pipelined together, but whereas MG-RAST is a user-friendly webserver that clusters rRNA sequences based on their similarity to create Operational Taxonomic Units (OTU), QIIME2 employs DADA2 in the construction of Amplicon Sequence Variants (ASV) by applying an error model that considers the abundance of each sequence and its similarity to other sequences. Taxonomic compositions obtained from the analyses of amplicon sequences of DNA from swine intestinal gut and faecal microbiota samples using MG-RAST and QIIME2 were compared at domain-, phylum-, family- and genus-levels in terms of richness, relative abundance and diversity. We found significant differences between the microbiota profiles obtained from each pipeline. At domain level, bacteria were relatively more abundant using QIIME2 than MG-RAST; at phylum level, seven taxa were identified exclusively by QIIME2; at family level, samples processed in QIIME2 showed higher evenness and richness (assessed by Shannon and Simpson indices). The genus-level compositions obtained from each pipeline were used in partial least squares-discriminant analyses (PLS-DA) to discriminate between sample collection sites (caecum, colon and faeces). The results showed that different genera were found to be significant for the models, based on the Variable Importance in Projection, e.g. when using sequencing data processed by MG-RAST, the three most important genera were Acetitomaculum, Ruminococcus and Methanosphaera, whereas when data was processed using QIIME2, these were Candidatus Methanomethylophilus, Sphaerochaeta and Anaerorhabdus. Furthermore, the application of differential filtering procedures before the PLS-DA revealed higher accuracy when using non-restricted datasets obtained from MG-RAST, whereas datasets obtained from QIIME2 resulted in more accurate discrimination of sample collection sites after removing genera with low relative abundances (<1%) from the datasets. Our results highlight the differences in taxonomic compositions of samples obtained from the two separate pipelines, while underlining the impact on downstream analyses, such as biomarkers identification.

环境微生物组研究依靠快速准确的生物信息学工具来表征基于 16S rRNA 基因的样品的分类组成。MetaGenome Rapid Annotation using Subsystem Technology (MG-RAST) 和 Quantitative Insights Into Microbial Ecology 2 (QIIME2) 是可用于执行此任务的两种最流行的工具。它们的底层算法在许多方面都不同，因此管道的比较提供了对它们的最佳使用和对结果的解释的见解。这两种生物信息学工具都基于几个流水线化的专业算法，而 MG-RAST 是一个用户友好的网络服务器，它根据 rRNA 序列的相似性对它们进行聚类以创建操作分类单元(OTU)，QIIME2 通过应用考虑每个序列的丰度及其与其他序列的相似性的误差模型，在构建扩增子序列变体 (ASV) 时使用 DADA2。从猪肠道和粪便 DNA 扩增子序列分析中获得的分类学组成使用 MG-RAST 和 QIIME2 的微生物群样本在域、门、科和属级别的丰富度、相对丰度和多样性方面进行了比较。我们发现从每个管道获得的微生物群分布之间存在显着差异。在域水平上，使用 QIIME2 的细菌比使用 MG-RAST 的细菌更丰富；在门水平上，QIIME2 专门鉴定了七个分类群；在家庭层面，在 QIIME2 中处理的样本显示出更高的均匀度和丰富度（由香农和辛普森指数评估）。从每个管道获得的属级组成用于偏最小二乘判别分析 (PLS-DA)，以区分样本收集部位（盲肠、结肠和粪便）。结果表明，基于投影中的变量重要性，发现不同的属对模型是显着的，Acetitomaculum，瘤胃球菌属和Methanosphaera，而当数据使用QIIME2被处理，这些是暂定Methanomethylophilus，Sphaerochaeta和Anaerorhabdus. 此外，当使用从 MG-RAST 获得的非限制性数据集时，PLS-DA 之前的差分过滤程序的应用显示出更高的准确度，而从 QIIME2 获得的数据集在去除相对丰度低的属后导致对样本收集位点的更准确区分。 <1%) 来自数据集。我们的结果突出了从两个独立管道中获得的样本在分类学组成上的差异，同时强调了对下游分析的影响，例如生物标志物识别。