SBGN Bricks Ontology as a tool to describe recurring concepts in molecular networks.


Journal

Briefings in bioinformatics
ISSN: 1477-4054
Titre abrégé: Brief Bioinform
Pays: England
ID NLM: 100912837

Informations de publication

Date de publication:
02 09 2021
Historique:
received: 02 11 2020
revised: 13 01 2021
pubmed: 25 3 2021
medline: 20 11 2021
entrez: 24 3 2021
Statut: ppublish

Résumé

A comprehensible representation of a molecular network is key to communicating and understanding scientific results in systems biology. The Systems Biology Graphical Notation (SBGN) has emerged as the main standard to represent such networks graphically. It has been implemented by different software tools, and is now largely used to communicate maps in scientific publications. However, learning the standard, and using it to build large maps, can be tedious. Moreover, SBGN maps are not grounded on a formal semantic layer and therefore do not enable formal analysis. Here, we introduce a new set of patterns representing recurring concepts encountered in molecular networks, called SBGN bricks. The bricks are structured in a new ontology, the Bricks Ontology (BKO), to define clear semantics for each of the biological concepts they represent. We show the usefulness of the bricks and BKO for both the template-based construction and the semantic annotation of molecular networks. The SBGN bricks and BKO can be freely explored and downloaded at sbgnbricks.org.

Identifiants

pubmed: 33758926
pii: 6184415
doi: 10.1093/bib/bbab049
pmc: PMC8425392
pii:
doi:

Substances chimiques

IRS1 protein, human 0
Insulin 0
Insulin Receptor Substrate Proteins 0
Protein Isoforms 0
Receptors, Somatomedin 0
Somatomedins 0
Mitogen-Activated Protein Kinases EC 2.7.11.24

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

© The Author(s) 2021. Published by Oxford University Press.

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Auteurs

Adrien Rougny (A)

Biotechnology Research Institute for Drug Discovery, National Institute of Advanced Industrial Science and Technology (AIST), Aomi, Tokyo, Japan.
The Donnelly Centre, University of Toronto, M5S 3E1, Toronto, Canada.
Com. Bio Big Data Open Innovation Lab. (CBBD-OIL), AIST, Aomi, Tokyo, Japan.

Vasundra Touré (V)

Norwegian University of Science and Technology (NTNU), Høgskoleringen 5, Realfagbygget, 7491 Trondheim, Norway.

John Albanese (J)

R. D. Berlin Center for Cell Analysis and Modeling, University of Connecticut School of Medicine, Farmington, CT 06030, USA.

Dagmar Waltemath (D)

Medical Informatics Laboratory, Institute for Community Medicine, University Medicine Greifswald, D-17475 Greifswald, Germany.

Denis Shirshov (D)

European Institute for Systems Biology and Medicine, CIRI UMR5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, 50 Avenue Tony Garnier, 69007 Lyon, France.
Institute of Cell Biophysics, Russian Academy of Sciences, 3 Institutskaya Street, Pushchino, Moscow Region, 142290, Russia.

Anatoly Sorokin (A)

Institute of Cell Biophysics, Russian Academy of Sciences, 3 Institutskaya Street, Pushchino, Moscow Region, 142290, Russia.
Moscow Institute of Physics and Technology, 9 Institutsky per., Dolgoprudny, Moscow Region, 141700, Russia.
University of Liverpool, Liverpool L7 3EA, UK.

Gary D Bader (GD)

The Donnelly Centre, University of Toronto, M5S 3E1, Toronto, Canada.

Michael L Blinov (ML)

R. D. Berlin Center for Cell Analysis and Modeling, University of Connecticut School of Medicine, Farmington, CT 06030, USA.
The Donnelly Centre, University of Toronto, M5S 3E1, Toronto, Canada.

Alexander Mazein (A)

European Institute for Systems Biology and Medicine, CIRI UMR5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, 50 Avenue Tony Garnier, 69007 Lyon, France.
Institute of Cell Biophysics, Russian Academy of Sciences, 3 Institutskaya Street, Pushchino, Moscow Region, 142290, Russia.

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