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'TIME': A Web Application for Obtaining Insights into Microbial Ecology Using Longitudinal Microbiome Data.

Krishanu D Baksi1, Bhusan K Kuntal1,2,3, Sharmila S Mande1

  • 1Bio-Sciences R&D Division, TCS Research, Tata Consultancy Services Ltd., Pune, India.

Frontiers in Microbiology
|February 9, 2018
PubMed
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Temporal Insights into Microbial Ecology (TIME) is a new web framework for analyzing microbiome time series data. It offers advanced tools to visualize microbial community dynamics and predict ecological interactions over time.

Area of Science:

  • Microbiology
  • Computational Biology
  • Bioinformatics

Background:

  • Microbiome studies are rapidly expanding due to decreasing sequencing costs.
  • Longitudinal microbiome data offers unique insights into microbial community dynamics and responses to perturbations.
  • Analyzing temporal changes in microbial communities requires specialized tools.

Purpose of the Study:

  • To introduce TIME (Temporal Insights into Microbial Ecology), a web-based framework for analyzing microbiome time series data.
  • To provide tools for preprocessing, filtering, and visualizing longitudinal microbiome data.
  • To enable biological inferences from temporal microbial data using advanced time series analysis methods.

Main Methods:

  • TIME web-server accepts various input formats and includes data preprocessing and filtering options.
Keywords:
Granger causality algorithmclusteringcommunity statemicrobiometime seriesvisualizationweb server

Related Experiment Videos

  • Implements time series analysis methods such as Dynamic Time Warping, Granger Causality, and Dickey Fuller test.
  • Introduces a new metric for comparing metagenomic time series and incorporates causality graph analysis.

    • Main Results:

    • TIME facilitates interactive visualization of temporal variations in microbial communities.
    • The framework enables prediction of microbial competition and community structure using stationarity information.
    • Causality graph analysis helps identify influential taxa and potential taxonomic markers.

    Conclusions:

    • TIME provides a user-friendly platform for complex analysis of microbiome time series data.
    • The framework aids in understanding microbial social networks and responses to environmental changes.
    • TIME enhances biological inference from longitudinal microbiome studies.