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q2-longitudinal: Longitudinal and Paired-Sample Analyses of Microbiome Data
Author(s) -
Nicholas A. Bokulich,
Matthew R. Dillon,
Yilong Zhang,
Jai Ram Rideout,
Evan Bolyen,
Huilin Li,
Paul S. Albert,
J. Gregory Caporaso
Publication year - 2018
Publication title -
msystems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.931
H-Index - 39
ISSN - 2379-5077
DOI - 10.1128/msystems.00219-18
Subject(s) - longitudinal study , longitudinal data , microbiome , plug in , sample (material) , population , sampling (signal processing) , computer science , data science , statistics , data mining , biology , bioinformatics , medicine , mathematics , chemistry , filter (signal processing) , chromatography , computer vision , programming language , environmental health
Studies of host-associated and environmental microbiomes often incorporate longitudinal sampling or paired samples in their experimental design. Longitudinal sampling provides valuable information about temporal trends and subject/population heterogeneity, offering advantages over cross-sectional and pre-post study designs. To support the needs of microbiome researchers performing longitudinal studies, we developed q2-longitudinal, a software plugin for the QIIME 2 microbiome analysis platform (https://qiime2.org). The q2-longitudinal plugin incorporates multiple methods for analysis of longitudinal and paired-sample data, including interactive plotting, linear mixed-effects models, paired differences and distances, microbial interdependence testing, first differencing, longitudinal feature selection, and volatility analyses. The q2-longitudinal package (https://github.com/qiime2/q2-longitudinal) is open-source software released under a 3-clause Berkeley Software Distribution (BSD) license and is freely available, including for commercial use. IMPORTANCE Longitudinal sampling provides valuable information about temporal trends and subject/population heterogeneity. We describe q2-longitudinal, a software plugin for longitudinal analysis of microbiome data sets in QIIME 2. The availability of longitudinal statistics and visualizations in the QIIME 2 framework will make the analysis of longitudinal data more accessible to microbiome researchers.

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