IDREM: Interactive visualization of dynamic regulatory networks.

Jun Ding,James S. Hagood,Naftali Kaminski,Namasivayam Ambalavanan,Ziv Bar-Joseph,Dina Schneidman

doi:10.1371/journal.pcbi.1006019

Abstract

The Dynamic Regulatory Events Miner (DREM) software reconstructs dynamic regulatory networks by integrating static protein-DNA interaction data with time series gene expression data. In recent years, several additional types of high-throughput time series data have been profiled when studying biological processes including time series miRNA expression, proteomics, epigenomics and single cell RNA-Seq. Combining all available time series and static datasets in a unified model remains an important challenge and goal. To address this challenge we have developed a new version of DREM termed interactive DREM (iDREM). iDREM provides support for all data types mentioned above and combines them with existing interaction data to reconstruct networks that can lead to novel hypotheses on the function and timing of regulators. Users can interactively visualize and query the resulting model. We showcase the functionality of the new tool by applying it to microglia developmental data from multiple labs.

Highlights

The analysis and modeling of dynamic regulatory networks remains a major goal of systems biology
We illustrate the functionality of interactive DREM (iDREM) by applying it to reconstruct mouse microglia developmental regulatory networks from a diverse set of high throughput biological data types (S1 Table)
While the whole brain data may only partially overlap with the microglia profiles, since the focus here is on the methods and visualization, we have added that data to fully showcase the ability of iDREM to integrate and interactively visualize diverse types of time series data

Summary

Introduction

The analysis and modeling of dynamic regulatory networks remains a major goal of systems biology. In 2007, we presented the Dynamic Regulatory Events Miner (DREM) that was developed to integrate time series gene expression and static protein-DNA interaction data [15].

Results

Conclusion