BED: a Biological Entity Dictionary based on a graph data model

F1000Research
Patrice Godard, Jonathan van Eyll

Abstract

The understanding of molecular processes involved in a specific biological system can be significantly improved by combining and comparing different data sets 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, most of these tools do not combine the information provided by individual resources to increase the completeness of the mapping process. Also, deprecated identifiers from former versions 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 three 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. This design combined with a local installation of the graph...Continue Reading

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Datasets Mentioned

BETA
E-GEOD-13355

Methods Mentioned

BETA
Profiler

Software Mentioned

ProbeID
Affymetrix
BED R
macOS
R R
lsBedCache
BED R R Archive Network ( CRAN
BED
Rstudio
visNetwork

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