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iDREM: Interactive visualization of dynamic regulatory networks.

Jun Ding1, James S Hagood2, Namasivayam Ambalavanan3

  • 1Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.

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Summary

The new interactive DREM (iDREM) software integrates diverse time-series biological data to reconstruct dynamic regulatory networks. This tool aids in generating novel hypotheses about gene regulation and function.

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Area of Science:

  • Computational Biology
  • Systems Biology
  • Genomics

Background:

  • Dynamic regulatory networks are crucial for understanding biological processes.
  • Existing tools like DREM integrate gene expression and protein-DNA interactions.
  • New high-throughput data types (miRNA, proteomics, epigenomics, scRNA-Seq) present integration challenges.

Purpose of the Study:

  • To develop an enhanced software tool for integrating diverse time-series and static biological data.
  • To reconstruct comprehensive dynamic regulatory networks.
  • To enable hypothesis generation regarding regulator function and timing.

Main Methods:

  • Developed interactive DREM (iDREM), an updated version of DREM software.
  • iDREM integrates static protein-DNA interaction data with multiple high-throughput time series datasets.
  • Implemented interactive visualization and querying capabilities for model exploration.

Main Results:

  • iDREM successfully integrates various data types, including time series miRNA expression, proteomics, epigenomics, and single-cell RNA-Seq.
  • The tool reconstructs dynamic regulatory networks by combining diverse datasets.
  • Application to microglia developmental data demonstrated the tool's utility.

Conclusions:

  • iDREM offers a unified approach to model diverse biological data for network reconstruction.
  • The software facilitates the generation of novel hypotheses on gene regulation.
  • iDREM enhances the study of complex biological systems through interactive data exploration.