No abstract

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.

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:

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.

This study evaluates the transcriptionally active, dissimilatory sulfur- and arsenic-cycling components of the microbial community in alkaline, hypersaline Mono Lake, CA, USA. We sampled five depths spanning the redox gradient (10, 15, 18, 25 and 31 m) during maximum thermal stratification. We used custom databases to identify transcripts of genes encoding complex iron-sulfur molybdoenzyme (CISM) proteins, with a focus on arsenic (arrA, aioA and arxA) and sulfur cycling (dsrA, aprA and soxB), and assigned them to taxonomic bins. We also report on the distribution of transcripts related to the ars arsenic detoxification pathway. Transcripts from detoxification pathways were not abundant in oxic surface waters (10 m). Arsenic cycling in the suboxic and microaerophilic zones of the water column (15 and 18 m) was dominated by arsenite-oxidizing members of the Gammaproteobacteria most closely affiliated with Thioalkalivibrio and Halomonas, transcribing arxA. We observed a transition to arsenate-reducing bacteria belonging to the Deltaproteobacteria and Firmicutes transcribing arsenate reductase (arrA) in anoxic bottom waters of the lake (25 and 31 m). Sulfur cycling at 15 and 18 m was dominated by Gammaproteobacteria (Thioalkalivibrio and Thioalkalimicrobium) oxidizing reduced S species, with a transition to sulfate-reducing Deltaproteobacteria at 25 and 31 m. Genes related to arsenic and sulfur oxidation from Thioalkalivibrio were more highly transcribed at 15 m relative to other depths. Our data highlight the importance of Thioalkalivibrio to arsenic and sulfur biogeochemistry in Mono Lake and identify new taxa that appear capable of transforming arsenic.