BED: a Biological Entity Dictionary based on a graph data model [version 1; referees: awaiting peer review]

The understanding of molecular processes involved in a specific biological system can be significantly improved by combining and comparing different data set and knowledge resources. However these information sources often use different identification systems and an identifier conversion step is required before any integration effort. Mapping between identifiers is often provided by the reference information resources and several tools have been implemented to simplify their use. However these tools cannot be easily customized and optimized for any specific use. Also the information provided by different resources is not combined to increase the efficiency of the mapping process and deprecated identifiers from former version of databases are not taken into account. Finally finding automatically the most relevant path to map identifiers from one scope to the other is often not trivial. The Biological Entity Dictionary (BED) addresses these challenges by relying on a graph data model describing possible relationships between entities and their identifiers. This model has been implemented using Neo4j and an R package provides functions to query the graph but also to create and feed a custom instance of the database. Patrice Godard ( ) Corresponding author: [email protected] : Conceptualization, Methodology, Software, Validation, Writing – Original Draft Preparation, Writing – Review & Editing; Author roles: Godard P : Supervision, Validation, Writing – Review & Editing van Eyll J No competing interests were disclosed. Competing interests: Godard P and van Eyll J. How to cite this article: BED: a Biological Entity Dictionary based on a graph data model [version 1; referees: 2018, :195 (doi: ) awaiting peer review] F1000Research 7 10.12688/f1000research.13925.1 © 2018 Godard P and van Eyll J. This is an open access article distributed under the terms of the Copyright: Creative Commons Attribution , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Licence This work was entirely supported by UCB Pharma. The authors declared that no grants were involved in supporting this work. Grant information: 15 Feb 2018, :195 (doi: ) First published: 7 10.12688/f1000research.13925.1 1,2