MetaFetcheR

MetaFetcheR maps and reconciles small-molecule metabolite identifiers across multiple public databases to standardize metabolite representation and annotation for metabolomics analyses.

Key Features:

• Integration Across Databases: Links metabolite identifiers across the Human Metabolome Database (HMDB), Chemical Entities of Biological Interest (ChEBI), PubChem, Kyoto Encyclopedia of Genes and Genomes (KEGG), and LipidMaps.
• Identifier Reconciliation: Resolves inconsistencies and ambiguities in metabolite IDs to reduce mislabelled compounds across databases.
• Standardization of Annotations: Standardizes metabolite representation and annotation across integrated resources to improve consistency.
• R Package Implementation: Provides functionality as an R package for programmatic identifier mapping and reconciliation.
• Data-fetching Algorithms: Implements algorithms to fetch and reconcile metabolite information from multiple databases for diverse data-fetching use cases.
• Benchmarking: Demonstrated superior performance through benchmarking against two independent case studies using published datasets.

Scientific Applications:

• Metabolite Identification: Facilitates accurate metabolite identification by unifying identifiers and annotations across databases.
• Pathway Analysis: Supports pathway-level analyses by improving consistency of metabolite mappings used in pathway interpretation.
• Data Quality Improvement: Enhances data quality and reliability in downstream statistical analyses by reducing identifier discordance.

Methodology:

MetaFetcheR uses algorithms to fetch and reconcile metabolite information from HMDB, ChEBI, PubChem, KEGG, and LipidMaps to resolve identifier inconsistencies, and its performance was benchmarked against two independent case studies using published datasets.