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SyNDI: synchronous network data integration framework.

Erno Lindfors1, Jesse C J van Dam2, Carolyn Ming Chi Lam3

  • 1LifeGlimmer GmbH, Markelstrasse 38, 12163, Berlin, Germany. lindfors@lifeglimmer.com.

BMC Bioinformatics
|November 8, 2018
PubMed
Summary
This summary is machine-generated.

The Synchronous Network Data Integration (SyNDI) framework enables simultaneous visualization of multiple biological networks. This approach, integrated with bioinformatics tools, enhances biological insights from complex omics data.

Keywords:
CytoscapeGalaxyMycobacterium tuberculosisNetwork biologyStaphylococcus aureusSynchronous network visualizationSystems biologyWorkflow

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Area of Science:

  • Systems biology
  • Bioinformatics
  • Network analysis

Background:

  • Biological systems are modeled as networks of interacting biomolecules.
  • Omics data generate diverse networks representing various conditions, species, or algorithms.
  • Synchronous visualization of multiple networks is crucial for efficient exploration and analysis.

Purpose of the Study:

  • To develop a framework for synchronous visualization and integrated analysis of multiple biological networks.
  • To enhance biological insights by combining network visualization with complementary bioinformatics tools.

Main Methods:

  • Development of the Synchronous Network Data Integration (SyNDI) framework.
  • Implementation of SyncVis, a Cytoscape application for simultaneous multi-network visualization.
  • Integration of the framework with bioinformatics tools via the Galaxy platform.

Main Results:

  • Demonstrated functionality with three biological examples: cardiovascular disease risk, Staphylococcus aureus infection pathways, and Mycobacterium tuberculosis transcriptional adaptations.
  • Identified novel regulatory motifs and gained deeper insights into inflammatory responses and metabolic network connectivity.
  • SyNDI facilitates user-friendly exploration of complex biological networks.

Conclusions:

  • The SyNDI framework integrates synchronous network visualization with bioinformatics tools.
  • The framework is customizable, allowing users to add new tools and datasets to the Galaxy platform for tailored analysis.