ArticleBMC systems biology2011
Critical assessment of human metabolic pathway databases: a stepping stone for future integration.
Article in BMC systems biology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed.
- High-Quality Predicted Metabolite-Pathway Annotations Expand Pathway Coverage and Increase Enriched Pathway Detection Across Metabolomics Datasets.Metabolites · 2026Article
- High-Quality Predicted Pathway Annotations Greatly Improve Pathway Enrichment Analysis of Metabolomics Datasets.bioRxiv : the preprint server for biology · 2025Article
- Differential gene expression analysis pipelines and bioinformatic tools for the identification of specific biomarkers: A review.Computational and structural biotechnology journal · 2024Review
- Current approaches and outstanding challenges of functional annotation of metabolites: a comprehensive review.Briefings in bioinformatics · 2024Review
- Extending inherited metabolic disorder diagnostics with biomarker interaction visualizations.Orphanet journal of rare diseases · 2023Article
- Metabolic Pathway Analysis: Advantages and Pitfalls for the Functional Interpretation of Metabolomics and Lipidomics Data.Biomolecules · 2023Article
- On the influence of several factors on pathway enrichment analysis.Briefings in bioinformatics · 2022Review
- DecoPath: a web application for decoding pathway enrichment analysis.NAR genomics and bioinformatics · 2021Article
- Pathway analysis in metabolomics: Recommendations for the use of over-representation analysis.PLoS computational biology · 2021Article
- Ten simple rules for creating reusable pathway models for computational analysis and visualization.PLoS computational biology · 2021Article
- Pathway size matters: the influence of pathway granularity on over-representation (enrichment analysis) statistics.BMC genomics · 2021Article
- Predicting instances of pathway ontology classes for pathway integration.Journal of biomedical semantics · 2019Article
- PathMe: merging and exploring mechanistic pathway knowledge.BMC bioinformatics · 2019Article
- ComPath: an ecosystem for exploring, analyzing, and curating mappings across pathway databases.NPJ systems biology and applications · 2019Article
- Efficient Reconstruction of Predictive Consensus Metabolic Network Models.PLoS computational biology · 2016Article
- Taking Bioinformatics to Systems Medicine.Methods in molecular biology (Clifton, N.J.) · 2016Review
- Many InChIs and quite some feat.Journal of computer-aided molecular design · 2015Article
- The Human Blood Metabolome-Transcriptome Interface.PLoS genetics · 2015Article
- Quality assurance of metabolomics.ALTEX · 2015Article
- Comparison of human cell signaling pathway databases--evolution, drawbacks and challenges.Database : the journal of biological databases and curation · 2015Review
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5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundMultiple pathway databases are available that describe the human metabolic network and have proven their usefulness in many applications, ranging from the analysis and interpretation of high-throughput data to their use as a reference repository. However, so far the various human metabolic networks described by these databases have not been systematically compared and contrasted, nor has the extent to which they differ been quantified. For a researcher using these databases for particular analyses of human metabolism, it is crucial to know the extent of the differences in content and their underlying causes. Moreover, the outcomes of such a comparison are important for ongoing integration efforts.
resultsWe compared the genes, EC numbers and reactions of five frequently used human metabolic pathway databases. The overlap is surprisingly low, especially on reaction level, where the databases agree on 3% of the 6968 reactions they have combined. Even for the well-established tricarboxylic acid cycle the databases agree on only 5 out of the 30 reactions in total. We identified the main causes for the lack of overlap. Importantly, the databases are partly complementary. Other explanations include the number of steps a conversion is described in and the number of possible alternative substrates listed. Missing metabolite identifiers and ambiguous names for metabolites also affect the comparison.
conclusionsOur results show that each of the five networks compared provides us with a valuable piece of the puzzle of the complete reconstruction of the human metabolic network. To enable integration of the networks, next to a need for standardizing the metabolite names and identifiers, the conceptual differences between the databases should be resolved. Considerable manual intervention is required to reach the ultimate goal of a unified and biologically accurate model for studying the systems biology of human metabolism. Our comparison provides a stepping stone for such an endeavor.
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