BED

BED maps biological identifiers across databases to enable integration and comparison of diverse biological datasets.

Key Features:

• Graph Data Model: BED employs a graph-based data model to represent relationships between biological entities and their corresponding identifiers.
• Neo4j Implementation: The graph data model is implemented in Neo4j to enable efficient querying and manipulation of large identifier networks.
• R Package Integration: BED provides an R package with functions for querying the Neo4j graph and creating custom graph instances programmatically.
• Handling Deprecated Identifiers: BED preserves and maps deprecated identifiers from previous database versions to maintain historical identifier relationships.
• Local Neo4j and Caching: A local Neo4j installation combined with a caching system accelerates conversion of large identifier lists.

Scientific Applications:

• Comparative Genomics: Mapping identifiers across genome annotation databases to enable cross-dataset gene and feature comparisons.
• Multi-omic Integration: Harmonizing identifiers for genomics, transcriptomics, and proteomics datasets to support integrative analyses.
• Integrative Data Analysis: Consolidating identifiers from heterogeneous biological databases to support synthesis and cross-referencing of evidence across sources.

Methodology:

BED constructs a Neo4j graph database representing biological entities and identifiers as nodes and edges, supports dynamic querying and updating of relationships, provides programmatic access via an R package, and uses a local Neo4j instance with caching for efficient bulk identifier conversion.