Browsing by Author "Zinovyev, A."

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    cd2sbgnml: Bidirectional conversion between CellDesigner and SBGN formats
    (Oxford University Press, 2020-01) Balaur, I.; Roy, L.; Mazein, A.; Karaca, S. Gökberk; Doğrusöz, Uğur; Barillot, E.; Zinovyev, A.
    Motivation: CellDesigner is a well-established biological map editor used in many large-scale scientific efforts. However, the interoperability between the Systems Biology Graphical Notation (SBGN) Markup Language (SBGNML) and the CellDesigner’s proprietary Systems Biology Markup Language (SBML) extension formats remains a challenge due to the proprietary extensions used in CellDesigner files. Results: We introduce a library named cd2sbgnml and an associated web service for bidirectional conversion between CellDesigner’s proprietary SBML extension and SBGN-ML formats. We discuss the functionality of the cd2sbgnml converter, which was successfully used for the translation of comprehensive large-scale diagrams such as the RECON Human Metabolic network and the complete Atlas of Cancer Signalling Network, from the CellDesigner file format into SBGN-ML. Availability and implementation: The cd2sbgnml conversion library and the web service were developed in Java, and distributed under the GNU Lesser General Public License v3.0. The sources along with a set of examples are available on GitHub (https://github.com/sbgn/cd2sbgnml and https://github.com/sbgn/cd2sbgnml-webservice, respectively). Supplementary information: Supplementary data are available at Bioinformatics online.
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    Community-driven roadmap for integrated disease maps
    (Oxford University Press, 2018) Ostaszewski, M.; Gebel, S.; Kuperstein, I.; Mazein, A.; Zinovyev, A.; Doğrusöz, Uğur; Hasenauer, J.; Fleming, R. M. T.; Novere, N. L.; Gawron, P.; Ligon, T.; Niarakis, A.; Nickerson, D.; Weindl, D.; Balling, R.; Barillot, E.; Auffray, C.; Schneider, R.
    The Disease Maps Project builds on a network of scientific and clinical groups that exchange best practices, share information and develop systems biomedicine tools. The project aims for an integrated, highly curated and user-friendly platform for disease-related knowledge. The primary focus of disease maps is on interconnected signaling, metabolic and gene regulatory network pathways represented in standard formats. The involvement of domain experts ensures that the key disease hallmarks are covered and relevant, up-to-date knowledge is adequately represented. Expert-curated and computer readable, disease maps may serve as a compendium of knowledge, allow for data-supported hypothesis generation or serve as a scaffold for the generation of predictive mathematical models. This article summarizes the 2nd Disease Maps Community meeting, highlighting its important topics and outcomes. We outline milestones on the roadmap for the future development of disease maps, including creating and maintaining standardized disease maps; sharing parts of maps that encode common human disease mechanisms; providing technical solutions for complexity management of maps; and Web tools for in-depth exploration of such maps. A dedicated discussion was focused on mathematical modeling approaches, as one of the main goals of disease map development is the generation of mathematically interpretable representations to predict disease comorbidity or drug response and to suggest drug repositioning, altogether supporting clinical decisions.
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    Systems medicine disease maps: community-driven comprehensive representation of disease mechanisms
    (Nature Publishing Group, 2018) Mazein, A.; Ostaszewski, M.; Kuperstein, I.; Watterson, S.; Le Novère, N.; Lefaudeux, D.; De Meulder, B.; Pellet, J.; Balaur, I.; Saqi, M.; Nogueira, M. M.; He, F.; Parton, A.; Lemonnier, N.; Gawron, P.; Gebel, S.; Hainaut, P.; Ollert, M.; Doğrusöz, Uğur; Barillot, E.; Zinovyev, A.; Schneider, R.; Balling, R.; Auffray, C.
    The development of computational approaches in systems biology has reached a state of maturity that allows their transition to systems medicine. Despite this progress, intuitive visualisation and context-dependent knowledge representation still present a major bottleneck. In this paper, we describe the Disease Maps Project, an effort towards a community-driven computationally readable comprehensive representation of disease mechanisms. We outline the key principles and the framework required for the success of this initiative, including use of best practices, standards and protocols. We apply a modular approach to ensure efficient sharing and reuse of resources for projects dedicated to specific diseases. Community-wide use of disease maps will accelerate the conduct of biomedical research and lead to new disease ontologies defined from mechanism-based disease endotypes rather than phenotypes.