Microbiome research is now moving beyond the compositional analysis of microbial taxa in a sample. Increasing evidence from large human microbiome studies suggests that functional consequences of changes in the intestinal microbiome may provide more power for studying their impact on inflammation and immune responses. Although 16S rRNA analysis is one of the most popular and a cost-effective method to profile the microbial compositions, marker-gene sequencing cannot provide direct information about the functional genes that are present in the genomes of community members. Bioinformatic tools have been developed to predict microbiome function with 16S rRNA gene data. Among them, PICRUSt2 has become one of the most popular functional profile prediction tools, which generates community-wide pathway abundances. However, no state-of-art inference tools are available to test the differences in pathway abundances between comparison groups. We have developed ggpicrust2, an R package, to do extensive differential abundance(DA) analyses and provide publishable visualization to highlight the signals.

翻译：微生物组研究现已超越对样本中微生物类群组成的传统分析。大量人类微生物组研究的新证据表明，肠道微生物组变化的功能后果可能为研究其对炎症和免疫反应的影响提供更强有力的依据。尽管16S rRNA分析是表征微生物组成最流行且经济高效的方法之一，但标记基因测序无法直接提供群落成员基因组中存在的功能基因信息。已有生物信息学工具可基于16S rRNA基因数据预测微生物组功能，其中PICRUSt2已成为最流行的功能谱预测工具之一，能够生成群落水平的通路丰度数据。然而，目前尚无先进的推断工具可用于检验比较组间通路丰度的差异。我们开发了ggpicrust2这款R包，可进行全面的差异丰度分析，并提供可发表级别的可视化结果以凸显关键信号。