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Plant specialized metabolites have ecological functions, yet the presence of numerous uncharacterized biosynthetic genes in plant genomes suggests that many molecules remain unknown. We discovered a triterpene biosynthetic network in the roots of the small mustard plant Arabidopsis thaliana. Collectively, we have elucidated and reconstituted three divergent pathways for the biosynthesis of root triterpenes, namely thalianin (seven steps), thalianyl medium-chain fatty acid esters (three steps), and arabidin (five steps). A. thaliana mutants disrupted in the biosynthesis of these compounds have altered root microbiota. In vitro bioassays with purified compounds reveal selective growth modulation activities of pathway metabolites toward root microbiota members and their biochemical transformation and utilization by bacteria, supporting a role for this biosynthetic network in shaping an Arabidopsis-specific root microbial community.

We present QIIME 2, an open-source microbiome data science platform accessible to users spanning the microbiome research ecosystem, from scientists and engineers to clinicians and policy makers. QIIME 2 provides new features that will drive the next generation of microbiome research. These include interactive spatial and temporal analysis and visualization tools, support for metabolomics and shotgun metagenomics analysis, and automated data provenance tracking to ensure reproducible, transparent microbiome data science.

Advances in high-throughput sequencing (HTS) have fostered rapid developments in the field of microbiome research, and massive microbiome datasets are now being generated. However, the diversity of software tools and the complexity of analysis pipelines make it difficult to access this field. Here, we systematically summarize the advantages and limitations of microbiome methods. Then, we recommend specific pipelines for amplicon and metagenomic analyses, and describe commonly-used software and databases, to help researchers select the appropriate tools. Furthermore, we introduce statistical and visualization methods suitable for microbiome analysis, including alpha- and beta-diversity, taxonomic composition, difference comparisons, correlation, networks, machine learning, evolution, source tracing, and common visualization styles to help researchers make informed choices. Finally, a step-by-step reproducible analysis guide is introduced. We hope this review will allow researchers to carry out data analysis more effectively and to quickly select the appropriate tools in order to efficiently mine the biological significance behind the data.

In the version of this article initially published, some reference citations were incorrect. The three references to Jupyter Notebooks should have cited Kluyver et al. instead of Gonzalez et al. The reference to Qiita should have cited Gonzalez et al. instead of Schloss et al. The reference to mothur should have cited Schloss et al. instead of McMurdie & Holmes. The reference to phyloseq should have cited McMurdie & Holmes instead of Huber et al. The reference to Bioconductor should have cited Huber et al. instead of Franzosa et al. And the reference to the biobakery suite should have cited Franzosa et al. instead of Kluyver et al. The errors have been corrected in the HTML and PDF versions of the article.

Data visualization plays a crucial role in illustrating results and sharing knowledge among researchers. Though many types of visualization tools are widely used, most of them require enough coding experience or are designed for specialized usages, or are not free. Here, we present ImageGP, a specialized visualization platform designed for biology and chemistry data illustration. Highlights• Publication-quality visualization results.• Easy to use and customize.• Reproducible results with scripts.

We present QIIME 2, an opensource microbiome data science platform accessible to users spanning the microbiome research ecosystem, from scientists and engineers to clinicians and policy makers. QIIME 2 provides new features that will drive the next generation of microbiome research. These include interactive spatial and temporal analysis and visualization tools, support for metabolomics and shotgun metagenomics analysis, and automated data provenance tracking to ensure reproducible, transparent microbiome data science.PeerJ Preprints | https://doi.org/10.7287/peerj.preprints.27295v2 | CC BY 4.0 Open Access | rec: