TY - JOUR
T1 - Qiita
T2 - rapid, web-enabled microbiome meta-analysis
AU - Gonzalez, Antonio
AU - Navas-Molina, Jose A.
AU - Kosciolek, Tomasz
AU - McDonald, Daniel
AU - Vázquez-Baeza, Yoshiki
AU - Ackermann, Gail
AU - DeReus, Jeff
AU - Janssen, Stefan
AU - Swafford, Austin D.
AU - Orchanian, Stephanie B.
AU - Sanders, Jon G.
AU - Shorenstein, Joshua
AU - Holste, Hannes
AU - Petrus, Semar
AU - Robbins-Pianka, Adam
AU - Brislawn, Colin J.
AU - Wang, Mingxun
AU - Rideout, Jai Ram
AU - Bolyen, Evan
AU - Dillon, Matthew
AU - Caporaso, J. Gregory
AU - Dorrestein, Pieter C.
AU - Knight, Rob
N1 - Funding Information:
We are grateful to J. Debelius, J. Jansson, D. Bazaldua, and J. Kuczynski for their help in improving Qiita via suggestion, code changes, and contributed datasets, or during the preparation of this manuscript; and to J. Gordon and his laboratory for helpful discussions. This work was supported in part by the Alfred P. Sloan Foundation (2017-9838 and 2015-13933 (R.K.)), the NIH/NIDDK (P01DK078669 (R.K.)), the NSF (DBI-1565057 and 1565100 (J.G.C. and R.K.)), the Office of Naval Research (ONR; N00014-15-1-2809 (R.K.)), and the US Army (CDMRP W81XWH-15-1-0653 (R.K.)).
Publisher Copyright:
© 2018, The Author(s), under exclusive licence to Springer Nature America, Inc.
PY - 2018/10/1
Y1 - 2018/10/1
N2 - Multi-omic insights into microbiome function and composition typically advance one study at a time. However, in order for relationships across studies to be fully understood, data must be aggregated into meta-analyses. This makes it possible to generate new hypotheses by finding features that are reproducible across biospecimens and data layers. Qiita dramatically accelerates such integration tasks in a web-based microbiome-comparison platform, which we demonstrate with Human Microbiome Project and Integrative Human Microbiome Project (iHMP) data.
AB - Multi-omic insights into microbiome function and composition typically advance one study at a time. However, in order for relationships across studies to be fully understood, data must be aggregated into meta-analyses. This makes it possible to generate new hypotheses by finding features that are reproducible across biospecimens and data layers. Qiita dramatically accelerates such integration tasks in a web-based microbiome-comparison platform, which we demonstrate with Human Microbiome Project and Integrative Human Microbiome Project (iHMP) data.
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U2 - 10.1038/s41592-018-0141-9
DO - 10.1038/s41592-018-0141-9
M3 - Article
C2 - 30275573
AN - SCOPUS:85054059754
SN - 1548-7091
VL - 15
SP - 796
EP - 798
JO - Nature Methods
JF - Nature Methods
IS - 10
ER -