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

Patrice Godard1,2, Jonathan van Eyll2

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Summary

The Biological Entity Dictionary (BED) improves biological data integration by efficiently mapping diverse molecular identifiers. It overcomes limitations of existing tools by using a comprehensive graph model and handling deprecated IDs for better biological system understanding.

Keywords:
RNA-seqdatabasegenomicsidentifiersmicroarrayproteomicstranscriptomics

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Molecular Biology

Background:

  • Integrating diverse biological datasets is crucial for understanding complex molecular processes.
  • Existing identifier mapping tools often lack completeness and fail to account for deprecated database entries.
  • Navigating identifier conversion across different biological databases can be complex and non-trivial.

Purpose of the Study:

  • To develop a robust solution for comprehensive biological identifier mapping.
  • To address the limitations of existing tools in handling deprecated identifiers and ensuring mapping completeness.
  • To facilitate efficient integration of disparate biological data sources.

Main Methods:

  • Implementation of a graph data model using Neo4j to represent relationships between biological entities and their identifiers.
  • Development of an R package for querying and managing the graph database.
  • Utilizing a local installation with a cache system for high-efficiency identifier conversion.

Main Results:

  • The Biological Entity Dictionary (BED) effectively maps identifiers across different biological databases.
  • BED successfully integrates information from multiple resources, increasing mapping completeness.
  • The system efficiently handles deprecated identifiers, a common issue with legacy data.
  • BED demonstrates high performance in converting large lists of identifiers.

Conclusions:

  • The Biological Entity Dictionary (BED) provides an efficient and comprehensive solution for biological identifier mapping.
  • BED enhances the integration of diverse biological data, advancing molecular systems understanding.
  • The graph-based approach and R package offer a flexible and powerful tool for bioinformaticians.