A new grid- and modularity-based layout algorithm for complex biological networks

PLoS One. 2019 Aug 29;14(8):e0221620. doi: 10.1371/journal.pone.0221620. eCollection 2019.

Abstract

The visualization of biological networks is critically important to aid researchers in understanding complex biological systems and arouses interest in designing efficient layout algorithms to draw biological networks according to their topology structures, especially for those networks with potential modules. The algorithms of grid layout series have an advantage in generating compact layouts with overlap-free nodes compared to force-directed; however, extant grid layout algorithms have difficulty in drawing modular networks and often generate layouts of high visual complexity when applied to networks with dense or clustered connectivity structure. To specifically assist the study of modular networks, we propose a grid- and modularity-based layout algorithm (GML) that consists of three stages: network preprocessing, module layout and grid optimization. The algorithm can draw complex biological networks with or without predefined modules based on the grid layout algorithm. It also outperforms other existing grid-based algorithms in the measurement of computation performance, ratio of edge-edge/node-edge crossings, relative edge lengths, and connectivity F-measures. GML helps users to gain insight into the network global characteristics through module layout, as well as to discern network details with grid optimization. GML has been developed as a VisANT plugin (https://hscz.github.io/Biological-Network-Visualization/) and is freely available to the research community.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms*
  • Gene Regulatory Networks*
  • Metabolic Networks and Pathways*
  • Models, Biological*
  • Reproducibility of Results
  • Signal Transduction*

Grants and funding

The work presented in this paper was carried out thanks to the support of the National Natural Science Foundation of China [grant number 61472166]; the Youth Foundation Project of Humanities and Social Sciences Research of Ministry of Education in China [grant number 17YJC870011]; the Natural Science Foundation of Jiangsu Province of China [grant number BK20161199]; the Jiangsu Overseas Research & Training Program for University Prominent Young & Middle-aged Teachers and Presidents [grant number 201613]; and the Planning Foundation Project of Humanities and Social Sciences Research of Ministry of Education in China [grant number 19YJA870005]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.