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Jai Ram Rideout

Researcher at Northern Arizona University

Publications -  26
Citations -  15341

Jai Ram Rideout is an academic researcher from Northern Arizona University. The author has contributed to research in topics: Microbiome & Marker gene. The author has an hindex of 16, co-authored 26 publications receiving 7582 citations. Previous affiliations of Jai Ram Rideout include Icahn School of Medicine at Mount Sinai.

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Journal ArticleDOI

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.
Journal ArticleDOI

Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin

TL;DR: The results illustrate the importance of parameter tuning for optimizing classifier performance, and the recommendations regarding parameter choices for these classifiers under a range of standard operating conditions are made.
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

The Biological Observation Matrix (BIOM) format or: how I learned to stop worrying and love the ome-ome

TL;DR: The BIOM file format and the biom-format project are steps toward reducing the “bioinformatics bottleneck” that is currently being experienced in diverse areas of biological sciences, and will help us move toward the next phase of comparative omics where basic science is translated into clinical and environmental applications.
Journal ArticleDOI

Subsampled open-reference clustering creates consistent, comprehensive OTU definitions and scales to billions of sequences.

TL;DR: A performance-optimized algorithm for assigning marker gene sequences generated on next-generation sequencing platforms to operational taxonomic units (OTUs) for microbial community analysis is presented and it is shown that subsampled open-reference OTU picking yields results that are highly correlated with those generated by “classic” open- reference OTUpicking through comparisons on three well-studied datasets.