The Minimum Information about a Molecular Interaction Causal Statement (MI2CAST).
- Touré V Department of Biology, Norwegian University of Science and Technology (NTNU), Trondheim, Norway.
- Vercruysse S Department of Biology, Norwegian University of Science and Technology (NTNU), Trondheim, Norway.
- Acencio ML Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology (NTNU), Trondheim, Norway.
- Lovering RC Functional Gene Annotation, Preclinical and Fundamental Science, Institute of Cardiovascular Science, UCL, University College London, London, UK.
- Orchard S European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK.
- Bradley G Computational Biology, Functional Genomics, GSK, Stevenage, UK.
- Casals-Casas C Swiss-Prot group, SIB Swiss Institute of Bioinformatics, 1 Michel Servet, 1211 Geneva 4, Switzerland.
- Chaouiya C Aix Marseille Univ, CNRS, Centrale Marseille, I2M, Marseille, France.
- Del-Toro N European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK.
- Flobak Å Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology (NTNU), Trondheim, Norway.
- Gaudet P SIB Swiss Institute of Bioinformatics, Geneva, Switzerland.
- Hermjakob H European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK.
- Hoyt CT Enveda Therapeutics, 53225 Bonn, Germany.
- Licata L Department of Biology, University of Rome Tor Vergata, Via della Ricerca Scientifica, 00133 Rome, Italy.
- Lægreid A Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology (NTNU), Trondheim, Norway.
- Mungall CJ Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
- Niknejad A Vital-IT group, SIB Swiss Institute of Bioinformatics, Quartier Sorge, Amphipole building, 1015 Lausanne, Switzerland.
- Panni S University of Calabria, Dip. of Biology, Ecology and Earth Science, Via Pietro Bucci Cubo 6/C Rende (CS), Italy.
- Perfetto L European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK.
- Porras P European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK.
- Pratt D Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA.
- Saez-Rodriguez J Institute of Computational Biomedicine, Heidelberg University, Faculty of Medicine, 69120 Heidelberg, Germany.
- Thieffry D Institut de Biologie de l'ENS (IBENS), Département de biologie, École normale supérieure, CNRS, INSERM, Université PSL, 75005 Paris, France.
- Thomas PD Division of Bioinformatics, Department of Preventive Medicine, University of Southern California, Los Angeles, CA USA.
- Türei D Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA.
- Kuiper M Department of Biology, Norwegian University of Science and Technology (NTNU), Trondheim, Norway.
- 2020-07-09
Published in:
- Bioinformatics (Oxford, England). - 2020
Statistics and Probability
Computational Theory and Mathematics
Biochemistry
Molecular Biology
Computational Mathematics
Computer Science Applications
English
MOTIVATION
A large variety of molecular interactions occurs between biomolecular components in cells. When a molecular interaction results in a regulatory effect, exerted by one component onto a downstream component, a so-called 'causal interaction' takes place. Causal interactions constitute the building blocks in our understanding of larger regulatory networks in cells. These causal interactions and the biological processes they enable (e.g., gene regulation) need to be described with a careful appreciation of the underlying molecular reactions. A proper description of this information enables archiving, sharing, and reuse by humans and for automated computational processing. Various representations of causal relationships between biological components are currently used in a variety of resources.
RESULTS
Here, we propose a checklist that accommodates current representations, called the Minimum Information about a Molecular Interaction CAusal STatement (MI2CAST). This checklist defines both the required core information, as well as a comprehensive set of other contextual details valuable to the end user and relevant for reusing and reproducing causal molecular interaction information. The MI2CAST checklist can be used as reporting guidelines when annotating and curating causal statements, while fostering uniformity and interoperability of the data across resources.
AVAILABILITY
The checklist together with examples is accessible at https://github.com/MI2CAST/MI2CAST.
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
A large variety of molecular interactions occurs between biomolecular components in cells. When a molecular interaction results in a regulatory effect, exerted by one component onto a downstream component, a so-called 'causal interaction' takes place. Causal interactions constitute the building blocks in our understanding of larger regulatory networks in cells. These causal interactions and the biological processes they enable (e.g., gene regulation) need to be described with a careful appreciation of the underlying molecular reactions. A proper description of this information enables archiving, sharing, and reuse by humans and for automated computational processing. Various representations of causal relationships between biological components are currently used in a variety of resources.
RESULTS
Here, we propose a checklist that accommodates current representations, called the Minimum Information about a Molecular Interaction CAusal STatement (MI2CAST). This checklist defines both the required core information, as well as a comprehensive set of other contextual details valuable to the end user and relevant for reusing and reproducing causal molecular interaction information. The MI2CAST checklist can be used as reporting guidelines when annotating and curating causal statements, while fostering uniformity and interoperability of the data across resources.
AVAILABILITY
The checklist together with examples is accessible at https://github.com/MI2CAST/MI2CAST.
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
- Language
-
- English
- Open access status
- green
- Identifiers
-
- DOI 10.1093/bioinformatics/btaa622
- PMID 32637990
- Persistent URL
- https://sonar.ch/global/documents/28214
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