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Jordan E. Bisanz

Researcher at University of California, San Francisco

Publications -  61
Citations -  12461

Jordan E. Bisanz is an academic researcher from University of California, San Francisco. The author has contributed to research in topics: Microbiome & Gut flora. The author has an hindex of 22, co-authored 49 publications receiving 5621 citations. Previous affiliations of Jordan E. Bisanz include Foundation University, Islamabad & University of Otago.

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Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2

Evan Bolyen, +123 more
- 01 Aug 2019 - 
TL;DR: QIIME 2 development was primarily funded by NSF Awards 1565100 to J.G.C. and R.K.P. and partial support was also provided by the following: grants NIH U54CA143925 and U54MD012388.
Posted ContentDOI

QIIME 2: Reproducible, interactive, scalable, and extensible microbiome data science

Evan Bolyen, +119 more
- 24 Oct 2018 - 
TL;DR: QIIME 2 provides new features that will drive the next generation of microbiome research, including 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.
Journal ArticleDOI

Discovery and inhibition of an interspecies gut bacterial pathway for Levodopa metabolism.

TL;DR: The major proposed pathway involves an initial decarboxylation of l-dopa to dopamine, followed by conversion of dopamine to m-tyramine by means of a distinctly microbial dehydroxylation reaction.
Journal ArticleDOI

Author Correction: Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2.

Evan Bolyen, +125 more
- 01 Sep 2019 - 
TL;DR: An amendment to this paper has been published and can be accessed via a link at the top of the paper.
Journal ArticleDOI

Meta-Analysis Reveals Reproducible Gut Microbiome Alterations in Response to a High-Fat Diet

TL;DR: It is demonstrated that a high-fat diet (HFD) reproducibly changes gut microbial community structure and the utility of microbiome meta-analyses in identifying robust and reproducible features for mechanistic studies in preclinical models is demonstrated.