In recent years, whole shotgun metagenomics (WSM) of complex microbial communities has become an established technology to perform compositional analyses of complex microbial communities, an approach that is heavily reliant on bioinformatic pipelines to process and interpret the generated raw sequencing data. However, the use of such in silico pipelines for the microbial taxonomic classification of short sequences may lead to significant errors in the compositional outputs deduced from such sequencing data. To investigate the ability of such in silico pipelines, we employed two commonly applied bioinformatic tools, i.e., MetaPhlAn2 and Kraken2 together with two metagenomic data sets originating from human and animal faecal samples. By using these bioinformatic programs that taxonomically classify WSM data based on marker genes, we observed a trend to depict a lower complexity of the microbial communities. Here, we assess the limitations of the most commonly employed bioinformatic pipelines, i.e., MetaPhlAn2 and Kraken2, and based on our findings, we propose that such analyses should ideally be combined with experimentally based microbiological validations.

中文翻译：

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微生物组现实检查：基于计算机的宏基因组学方法研究复杂细菌群落的局限性。

近年来，复杂微生物群落的完整散弹枪宏基因组学（WSM）已成为执行复杂微生物群落组成分析的一项成熟技术，该方法在很大程度上依赖于生物信息学管道来处理和解释所生成的原始测序数据。但是，将此类计算机模拟流水线用于短序列的微生物分类学分类可能会导致从此类测序数据推导出的成分输出中出现重大错误。调查这种计算机技术的能力在管道中，我们采用了两种常用的生物信息学工具，即MetaPhlAn2和Kraken2，以及来自人和动物粪便样本的两个宏基因组数据集。通过使用这些基于标记基因对WSM数据进行分类的生物信息学程序，我们观察到了一种趋势，该趋势描述了微生物群落复杂性降低的趋势。在这里，我们评估最常用的生物信息学管道（即MetaPhlAn2和Kraken2）的局限性，并根据我们的发现，我们建议将此类分析理想地与基于实验的微生物学验证相结合。