4.7 Article

q2-longitudinal: Longitudinal and Paired-Sample Analyses of Microbiome Data

Journal

MSYSTEMS
Volume 3, Issue 6, Pages -

Publisher

AMER SOC MICROBIOLOGY
DOI: 10.1128/mSystems.00219-18

Keywords

bioinformatics; linear mixed effects; longitudinal analysis; microbiome

Categories

Funding

  1. NSF [1565100]
  2. Partnership for Native American Cancer Prevention (NIH/NCI) [U54CA143924, U54CA143925]
  3. State of Arizona Technology and Research Initiative Fund (TRIF)
  4. NIH [R01DK110014]
  5. Direct For Biological Sciences
  6. Div Of Biological Infrastructure [1565100] Funding Source: National Science Foundation

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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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