Location: Genomics and Bioinformatics ResearchTitle: QIIME 2: Reproducible, interactive, scalable, and extensible microbiome data science
|BOLYEN, EVAN - Northern Arizona University|
|RIDEOUT, JAI RAM - Northern Arizona University|
|DILLON, MATTHEW - Northern Arizona University|
|BOKULICH, NICHOLAS - Northern Arizona University|
|ABNET, CHRISTIAN - National Cancer Institute (NCI, NIH)|
|AL-GHALITH, GABRIEL - University Of Minnesota|
|ALEXANDER, HARRIET - Woods Hole Oceanographic Institute (WHOI)|
|ALM, ERIC - Massachusetts Institute Of Technology|
|ARUMUGAM, MANIMOZHIYAN - University Of Copenhagen|
|ASNICAR, FRANCESCO - University Of Trento, Italy|
Submitted to: Nature Biotechnology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/24/2019
Publication Date: 7/24/2019
Citation: Bolyen, E., Rideout, J., Dillon, M., Bokulich, N., Abnet, C., Al-Ghalith, G., Alexander, H., Alm, E., Arumugam, M., Asnicar, F., Rivers, A.R. 2019. QIIME 2: Reproducible, interactive, scalable, and extensible microbiome data science. Nature Biotechnology. https://doi.org/10.1038/s41587-019-0209-9.
Interpretive Summary: We present QIIME 2, free software to enable scientists, engineers, clinicians and policy makers to analyze microbial communities and microbiomes. 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 a wide range of scientific data types including DNA and chemical data, and robust tracking systems to document how scientific results are produced.
Technical Abstract: 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.