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Closing the gap between formats for storing layout information in systems biology.

Authors
  • Hoksza, David1, 2
  • Gawron, Piotr1
  • Ostaszewski, Marek1
  • Hasenauer, Jan3, 4, 5
  • Schneider, Reinhard1
  • 1 Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 6, avenue du Swing L-4367 Belvaux, Luxembourg. , (Luxembourg)
  • 2 Faculty of Mathematics and Physics, Charles University, Malostranské nám. 25, 118 00 Prague, Czech Republic. , (Czechia)
  • 3 Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Ingolstädter Landstr. 1, 85764, Neuherberg, Germany. , (Germany)
  • 4 Department of Mathematics, Technische Universität München, München, Germany. , (Germany)
  • 5 Faculty of Mathematics and Natural Sciences, University of Bonn, Bonn, Germany. , (Germany)
Type
Published Article
Journal
Briefings in Bioinformatics
Publisher
Oxford University Press
Publication Date
Jul 15, 2020
Volume
21
Issue
4
Pages
1249–1260
Identifiers
DOI: 10.1093/bib/bbz067
PMID: 31273380
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

The understanding of complex biological networks often relies on both a dedicated layout and a topology. Currently, there are three major competing layout-aware systems biology formats, but there are no software tools or software libraries supporting all of them. This complicates the management of molecular network layouts and hinders their reuse and extension. In this paper, we present a high-level overview of the layout formats in systems biology, focusing on their commonalities and differences, review their support in existing software tools, libraries and repositories and finally introduce a new conversion module within the MINERVA platform. The module is available via a REST API and offers, besides the ability to convert between layout-aware systems biology formats, the possibility to export layouts into several graphical formats. The module enables conversion of very large networks with thousands of elements, such as disease maps or metabolic reconstructions, rendering it widely applicable in systems biology. © The Author(s) 2019. Published by Oxford University Press.

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