q2-longitudinal: Longitudinal and paired-sample analyses of microbiome data

Nicholas A. Bokulich, Matthew R. Dillon, Yilong Zhang, Jai Ram Rideout, Evan Bolyen, Huilin Li, Paul S. Albert, J. Gregory Caporaso

Research output: Contribution to journalArticlepeer-review

52 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article numbere00219-18
JournalmSystems
Volume3
Issue number6
DOIs
StatePublished - Dec 2018

Keywords

  • Bioinformatics
  • Linear mixed effects
  • Longitudinal analysis
  • Microbiome

ASJC Scopus subject areas

  • Microbiology
  • Ecology, Evolution, Behavior and Systematics
  • Biochemistry
  • Physiology
  • Modeling and Simulation
  • Molecular Biology
  • Genetics
  • Computer Science Applications

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