Robust Moiety Model Selection Using Mass Spectrometry Measured Isotopologues

Huan, Jianya; Moseley, Hunter N. B.

doi:10.1101/839241

Cited by 2 publications

(2 citation statements)

References 20 publications

(20 reference statements)

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“…Advances in analytical methodologies like mass spectroscopy and nuclear magnetic resonance greatly improve the high-throughput detection of thousands of metabolites, enabling the generation of large volumes of high-quality metabolomics datasets [ 8 , 9 ] that greatly facilitate metabolic research. As a next major step, incorporating reaction atom-mappings into metabolic models enables metabolic flux analysis of isotope-labeled metabolomics datasets [ 10 , 11 , 12 , 13 ], which will contribute to the large-scale characterization of metabolic flux molecular phenotypes and prediction of potential targets for gene manipulation [ 4 ]. Building reliable metabolic models heavily depend on the completeness of metabolic network databases.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Harmonization of Atom-Resolved Metabolic Reactions across Metabolic Databases

Huan

Moseley

2021

Metabolites

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Metabolic models have been proven to be useful tools in system biology and have been successfully applied to various research fields in a wide range of organisms. A relatively complete metabolic network is a prerequisite for deriving reliable metabolic models. The first step in constructing metabolic network is to harmonize compounds and reactions across different metabolic databases. However, effectively integrating data from various sources still remains a big challenge. Incomplete and inconsistent atomistic details in compound representations across databases is a very important limiting factor. Here, we optimized a subgraph isomorphism detection algorithm to validate generic compound pairs. Moreover, we defined a set of harmonization relationship types between compounds to deal with inconsistent chemical details while successfully capturing atom-level characteristics, enabling a more complete enabling compound harmonization across metabolic databases. In total, 15,704 compound pairs across KEGG (Kyoto Encyclopedia of Genes and Genomes) and MetaCyc databases were detected. Furthermore, utilizing the classification of compound pairs and EC (Enzyme Commission) numbers of reactions, we established hierarchical relationships between metabolic reactions, enabling the harmonization of 3856 reaction pairs. In addition, we created and used atom-specific identifiers to evaluate the consistency of atom mappings within and between harmonized reactions, detecting some consistency issues between the reaction and compound descriptions in these metabolic databases.

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Section: Introductionmentioning

confidence: 99%

Hierarchical Harmonization of Atom-Resolved Metabolic Reactions across Metabolic Databases

Huan

Moseley

2021

Metabolites

Self Cite

View full text Add to dashboard Cite

show abstract

“…Advances in analytical methodologies like mass spectroscopy and nuclear magnetic resonance greatly improve the high-throughput detection of thousands of metabolites, enabling the generation of large volumes of high-quality metabolomics datasets [8,9] that greatly facilitate metabolic research. As a next major step, incorporating reaction atom-mappings into metabolic models enables metabolic flux analysis of isotope-labeled metabolomics datasets [10][11][12][13], which will contribute to the large-scale characterization of metabolic flux molecular phenotypes and prediction of potential targets for gene manipulation [4]. Building reliable metabolic models heavily depend on the completeness of metabolic network databases.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Harmonization of Atom-Resolved Metabolic Reactions Across Metabolic Databases

Huan

Moseley

2021

Preprint

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View full text Add to dashboard Cite

Metabolic models have been proven to be useful tools in system biology and have been suc-cessfully applied to various research fields in a wide range of organisms. A relatively complete metabolic network is a prerequisite for deriving reliable metabolic models. The first step in con-structing metabolic network is to harmonize compounds and reactions across different metabolic databases. However, effectively integrating data from various sources still remains a big chal-lenge. Incomplete and inconsistent atomistic details in compound representations across data-bases is a very important limiting factor. Here, we optimized a subgraph isomorphism detection algorithm to validate generic compound pairs. Moreover, we defined a set of harmonization re-lationship types between compounds to deal with inconsistent chemical details while successfully capturing atom-level characteristics, enabling a more complete enabling compound harmoniza-tion across metabolic databases. In total, 15,704 compound pairs across KEGG (Kyoto Encyclo-pedia of Genes and Genomes) and MetaCyc databases were detected. Furthermore, utilizing the classification of compound pairs and EC (Enzyme Commission) numbers of reactions, we estab-lished hierarchical relationships between metabolic reactions, enabling the harmonization of 3,856 reaction pairs. In addition, we created and used atom-specific identifiers to evaluate the con-sistency of atom mappings within and between harmonized reactions, detecting some con-sistency issues between the reaction and compound descriptions in these metabolic databases.

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Robust Moiety Model Selection Using Mass Spectrometry Measured Isotopologues

Abstract: model selection. Furthermore, curation efforts of public metabolomics repositories to maintain high data quality could have huge impacts on future metabolic modeling efforts.

Cited by 2 publications

References 20 publications

Hierarchical Harmonization of Atom-Resolved Metabolic Reactions across Metabolic Databases

Hierarchical Harmonization of Atom-Resolved Metabolic Reactions across Metabolic Databases

Hierarchical Harmonization of Atom-Resolved Metabolic Reactions Across Metabolic Databases

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