Accurate and Comprehensive Mapping of Multi-omic Data to Biological Pathways

Importance of the Topic

Pathway analysis integrates differential entities from genomics, transcriptomics, proteomics, and metabolomics into biological context to derive mechanistic insights. Accurate mapping of experimental identifiers to pathway databases is essential to avoid missing key enrichments and enable robust interpretation across multiple resources.

Study Objectives and Overview

This application note evaluates the Agilent-BridgeDB technology within GeneSpring/Mass Profiler Professional (MPP) and its Pathway Architect module. Through four representative case studies, it demonstrates how BridgeDB overcomes identifier inconsistencies, enantiomer resolution, and incomplete annotations to enhance pathway mapping across KEGG, BioCyc, and WikiPathways for multi-omic experiments.

Methodology and Instrumentation

• Software Framework: Agilent-BridgeDB integrated into GeneSpring/MPP Pathway Architect
• Gene/Protein Mapper: Organism-specific files from the Gladstone Institute derived from Ensembl
• Metabolite Mapper: Universal Agilent Technologies database linking metabolites across multiple identifier schemes
• Supported Formats: BioPAX and GPML for pathway imports from KEGG, BioCyc, and WikiPathways
• Identifier Bridging: BridgeDB mapper files align synonyms and database IDs (KEGG, HMDB, ChEBI, UniProt, Entrez, etc.)

Key Results and Discussion

• Case Study 1 resolved missing Entrez Gene IDs by mapping UniProt identifiers, enabling detection of tryptophan synthase in BioCyc
• Case Study 2 merged enantiomer-specific metabolite IDs (L-/D-cysteine) to a generic experimental annotation, improving matching in KEGG pathways
• Case Study 3 combined metabolomic and transcriptomic data across BioCyc and KEGG, revealing complementary pathway enrichment otherwise overlooked
• Case Study 4 utilized multiple annotation columns (RefSeq, UniGene, Ensembl) to map sparsely annotated genes, ensuring comprehensive entity coverage

Benefits and Practical Applications

BridgeDB significantly increases mapping accuracy and completeness in multi-omic studies, leading to:

• Enhanced pathway enrichment and mechanistic insights
• Reduced false negatives due to annotation gaps or identifier mismatches
• Streamlined workflows for researchers analyzing complex datasets

Future Trends and Opportunities

• Expansion of mapper databases to include emerging ontologies and new pathway repositories
• Integration with machine learning for automated identifier reconciliation
• Improved community standards for universal identifiers to minimize bridging requirements
• Real-time updating of mapper files via cloud-based services to reflect the latest database releases

Conclusion

Agilent-BridgeDB within GeneSpring/MPP provides a robust solution for accurate and comprehensive mapping of multi-omic data to biological pathways. By bridging identifier discrepancies and supporting diverse pathway sources, it enables deeper biological interpretation and more effective experimental planning.

References

1. Kanehisa M et al. KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res, 42:D199–D205 (2014)
2. Caspi R et al. The MetaCyc database of metabolic pathways and enzymes. Nucleic Acids Res, 38:D473–D479 (2010)
3. Kelder T et al. WikiPathways: building research communities on biological pathways. Nucleic Acids Res, 40:D1301–D1307 (2012)
4. Stobbe MD et al. A review of pathway databases. BMC Syst Biol, 5:165 (2011)
5. Soh MK et al. Comparative analysis of pathway database coverage. BMC Bioinformatics, 11:449 (2010)
6. Altman RB et al. Integrative analysis of pathway databases. BMC Bioinformatics, 14:112 (2013)
7. van Iersel MP et al. The BridgeDb framework: standardized identifier mapping. BMC Bioinformatics, 11:5 (2010)
8. Flicek P et al. Ensembl 2014. Nucleic Acids Res, 42:D749–D755 (2014)