standards > reporting guideline > DOI:10.25504/FAIRsharing.zpCPN7


ready Minimum Information about a Molecular Interaction Causal Statement

Abbreviation: MI2CAST


General Information
The Minimum Information about a Molecular Interaction Causal Statement (MI2CAST) guidelines describe the minimum and necessary information to depict causal interactions in molecular biology, as well as contextual details. The aim is to homogenize their representation for better usability and understanding, by making the data “FAIR” (Findable, Accessible, Interoperable and Reproducible). This checklist considers terms used in different formats (e.g., PSI-MITAB2.8, BEL, GO-CAM) and covers the full range of metadata that should ideally be annotated during the curation process to enrich the description of a molecular causal interaction. Complying with these guidelines should be considered as good practice for the annotation of causal statements to generate high quality statements.

Homepage https://github.com/MI2CAST/MI2CAST

Countries that developed this resource Norway

Created in 2020

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How to cite this record FAIRsharing.org: MI2CAST; Minimum Information about a Molecular Interaction Causal Statement; DOI: https://doi.org/10.25504/FAIRsharing.zpCPN7; Last edited: July 31, 2020, 2:06 p.m.; Last accessed: Sep 30 2020 10:14 p.m.


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Record added: June 7, 2020, 10:12 a.m.
Record updated: July 12, 2020, 11 a.m. by vtoure.

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The Minimum Information about a Molecular Interaction Causal Statement (MI2CAST)

Touré et al.
Bioinformatics 2020

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Related Databases (3)
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PubMed is a search engine of biomedical literature, provided as a service of the U.S. National Library of Medicine and includes more than 25 million citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

SIGnaling Network Open Resource
SIGNOR, the SIGnaling Network Open Resource, organizes and stores in a structured format signaling information published in the scientific literature. The captured information is stored as binary causative relationships between biological entities and can be represented graphically as activity flow. The entire network can be freely downloaded and used to support logic modeling or to interpret high content datasets. The core of this project is a collection of more than 11000 manually-annotated causal relationships between proteins that participate in signal transduction. Each relationship is linked to the literature reporting the experimental evidence. In addition each node is annotated with the chemical inhibitors that modulate its activity. The signaling information is mapped to the human proteome even if the experimental evidence is based on experiments on mammalian model organisms.

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