Abstract

Visualizations of biomolecular networks assist in systems-level data exploration in many cellular processes. Data generated from high-throughput experiments increasingly inform these networks, yet current tools do not adequately scale with concomitant increase in their size and complexity. We present an open source software platform, interactome-CAVE (iCAVE), for visualizing large and complex biomolecular interaction networks in 3D. Users can explore networks (i) in 3D using a desktop, (ii) in stereoscopic 3D using 3D-vision glasses and a desktop, or (iii) in immersive 3D within a CAVE environment. iCAVE introduces 3D extensions of known 2D network layout, clustering, and edge-bundling algorithms, as well as new 3D network layout algorithms. Furthermore, users can simultaneously query several built-in databases within iCAVE for network generation or visualize their own networks (e.g., disease, drug, protein, metabolite). iCAVE has modular structure that allows rapid development by addition of algorithms, datasets, or features without affecting other parts of the code. Overall, iCAVE is the first freely available open source tool that enables 3D (optionally stereoscopic or immersive) visualizations of complex, dense, or multi-layered biomolecular networks. While primarily designed for researchers utilizing biomolecular networks, iCAVE can assist researchers in any field.

Highlights

  • Interaction networks are one of the primary visual metaphors for communicating and understanding -omics data at a systems level

  • We introduce interactome-Cave automatic virtual environment (CAVE), an open source tool for Three dimensions (3D), stereoscopic 3D, and immersive 3D visualizations of complex, large, and/or multi-layered networks. iCAVE development is made possible by the continuous evolution of data analysis tools in virtual reality (VR), stereoscopic visualization, and emerging 3D technologies

  • We introduce the algorithms we implemented in iCAVE for network layout and clustering and discuss input and output formats, as well as performance and scalability aspects

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Summary

Introduction

Interaction networks are one of the primary visual metaphors for communicating and understanding -omics data at a systems level. From cellular organisms to human society, networks provide critical clues on systems-level behavior [1,2,3]. Changes in networks have helped in prognosis for breast cancer patients [6], analyzing systematic inflammation in humans [8] or studying.

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