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TimeXNet Web: identifying cellular response networks from diverse omics time-course data.

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TimeXNet Web analyzes time-course omics data to reveal cellular response networks. This tool identifies novel regulators and pathways from transcriptomic, proteomic, and phospho-proteomic data for multiple species.

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Area of Science:

  • Systems biology
  • Bioinformatics
  • Network analysis

Background:

  • Time-course omics data are crucial for understanding cellular responses and signaling pathways.
  • Existing online tools often lack the capability to analyze diverse high-throughput time-course datasets simultaneously.

Purpose of the Study:

  • To develop TimeXNet Web, a novel web server for analyzing multiple types of high-throughput time-course omics data.
  • To extract time-dependent gene/protein response networks and identify key regulatory pathways.

Main Methods:

  • TimeXNet Web processes time-course transcriptomic, proteomic, or phospho-proteomic data alongside an input interaction network.
  • It classifies genes/proteins into time-dependent activity groups and maps probable paths between them.
  • The tool utilizes weighted protein-protein interaction networks for 12 model organisms.

Main Results:

  • The server generates a time-dependent response network, highlighting activated genes/proteins.
  • It identifies novel regulatory elements not apparent in the initial data.
  • Users can perform functional enrichment analysis on the derived response network.

Conclusions:

  • TimeXNet Web provides a comprehensive platform for dissecting complex cellular responses from multi-omics time-course data.
  • The tool facilitates the discovery of novel regulators and signaling pathways across various species.