Emerging computational tools and models for studying gut microbiota composition and function

[Display omitted] •Longitudinal approach offers insights unavailable from cross-sectional approach.•Analyzing sparse and unevenly sampled time-series data brings unique challenges.•Current methods and tools for analyzing multi-omics time-series data are critically reviewed. The gut microbiota and its metabolites play critical roles in human health and disease. Advances in high-throughput sequencing, mass spectrometry, and other omics assay platforms have improved our ability to generate large volumes of data exploring the temporal variations in the compositions and functions of microbial communities. To elucidate mechanisms, methods and tools are needed that can rigorously model the dependencies within time-series data. Longitudinal data are often sparse and unevenly sampled, and nontrivial challenges remain in determining statistical significance, normalization across different data types, and model validation. In this review, we highlight recent developments in models and software tools for the analysis of time series microbiome and metabolome data, as well as integration of these data.