Qiita: rapid, web-enabled microbiome meta-analysis

被引:0
|
作者
Antonio Gonzalez
Jose A. Navas-Molina
Tomasz Kosciolek
Daniel McDonald
Yoshiki Vázquez-Baeza
Gail Ackermann
Jeff DeReus
Stefan Janssen
Austin D. Swafford
Stephanie B. Orchanian
Jon G. Sanders
Joshua Shorenstein
Hannes Holste
Semar Petrus
Adam Robbins-Pianka
Colin J. Brislawn
Mingxun Wang
Jai Ram Rideout
Evan Bolyen
Matthew Dillon
J. Gregory Caporaso
Pieter C. Dorrestein
Rob Knight
机构
[1] University of California,Department of Pediatrics, School of Medicine
[2] San Diego,Department of Computer Science and Engineering
[3] University of California,Center for Microbiome Innovation
[4] San Diego,Department of Biology
[5] University of California,Department of Computer Science
[6] San Diego,Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences
[7] University of California,Pathogen and Microbiome Institute
[8] San Diego,Department of Biological Sciences
[9] University of Colorado,undefined
[10] Boulder,undefined
[11] Earth & Biological Sciences Directorate,undefined
[12] Pacific Northwest National Laboratory,undefined
[13] University of California,undefined
[14] San Diego,undefined
[15] Northern Arizona University,undefined
[16] Northern Arizona University,undefined
[17] Google LLC,undefined
[18] Inscripta,undefined
[19] Inc.,undefined
来源
Nature Methods | 2018年 / 15卷
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学科分类号
摘要
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.
引用
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页码:796 / 798
页数:2
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