微生物群
计算机科学
Web应用程序
数据科学
计算生物学
基因组
万维网
生物
生物信息学
遗传学
基因
作者
Antonio González,José A. Navas-Molina,Tomasz Kościółek,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 Brislawn,Mingxun Wang,Jai Ram Rideout,Evan Bolyen,Matthew R. Dillon,J. Gregory Caporaso,Pieter C. Dorrestein,Rob Knight
出处
期刊:Nature Methods
[Springer Nature]
日期:2018-09-20
卷期号:15 (10): 796-798
被引量:516
标识
DOI:10.1038/s41592-018-0141-9
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.
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