Dataset Information

DEMETER: Efficient simultaneous curation of genome-scale reconstructions guided by experimental data and refined gene annotations.

ABSTRACT: Manual curation of genome-scale reconstructions is laborious, yet existing automated curation tools do not typically take species-specific experimental and curated genomic data into account. We developed DEMETER, a COBRA Toolbox extension, that enables the efficient, simultaneous refinement of thousands of draft genome-scale reconstructions, while ensuring adherence to the quality standards in the field, agreement with available experimental data, and refinement of pathways based on manually refined genome annotations. DEMETER and tutorials are freely available at https://github.com/opencobra.

SUBMITTER: Heinken A

PROVIDER: S-EPMC8570805 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature

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DEMETER: efficient simultaneous curation of genome-scale reconstructions guided by experimental data and refined gene annotations.

Heinken Almut A Magnúsdóttir Stefanía S Fleming Ronan M T RMT Thiele Ines I

Bioinformatics (Oxford, England) 20211101 21

<h4>Motivation</h4>Manual curation of genome-scale reconstructions is laborious, yet existing automated curation tools do not typically take species-specific experimental and curated genomic data into account.<h4>Results</h4>We developed Data-drivEn METabolic nEtwork Refinement (DEMETER), a Constraint-Based Reconstruction and Analysis (COBRA) Toolbox extension, which enables the efficient, simultaneous refinement of thousands of draft genome-scale reconstructions, while ensuring adherence to the ...[more]

PMID: 34473240

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Project description:BackgroundCurrently, there exists tens of different microbial and eukaryotic metabolic reconstructions (e.g., Escherichia coli, Saccharomyces cerevisiae, Bacillus subtilis) with many more under development. All of these reconstructions are inherently incomplete with some functionalities missing due to the lack of experimental and/or homology information. A key challenge in the automated generation of genome-scale reconstructions is the elucidation of these gaps and the subsequent generation of hypotheses to bridge them.ResultsIn this work, an optimization based procedure is proposed to identify and eliminate network gaps in these reconstructions. First we identify the metabolites in the metabolic network reconstruction which cannot be produced under any uptake conditions and subsequently we identify the reactions from a customized multi-organism database that restores the connectivity of these metabolites to the parent network using four mechanisms. This connectivity restoration is hypothesized to take place through four mechanisms: a) reversing the directionality of one or more reactions in the existing model, b) adding reaction from another organism to provide functionality absent in the existing model, c) adding external transport mechanisms to allow for importation of metabolites in the existing model and d) restore flow by adding intracellular transport reactions in multi-compartment models. We demonstrate this procedure for the genome- scale reconstruction of Escherichia coli and also Saccharomyces cerevisiae wherein compartmentalization of intra-cellular reactions results in a more complex topology of the metabolic network. We determine that about 10% of metabolites in E. coli and 30% of metabolites in S. cerevisiae cannot carry any flux. Interestingly, the dominant flow restoration mechanism is directionality reversals of existing reactions in the respective models.ConclusionWe have proposed systematic methods to identify and fill gaps in genome-scale metabolic reconstructions. The identified gaps can be filled both by making modifications in the existing model and by adding missing reactions by reconciling multi-organism databases of reactions with existing genome-scale models. Computational results provide a list of hypotheses to be queried further and tested experimentally.

Dataset Information

DEMETER: Efficient simultaneous curation of genome-scale reconstructions guided by experimental data and refined gene annotations.

Publications

DEMETER: efficient simultaneous curation of genome-scale reconstructions guided by experimental data and refined gene annotations.

Similar Datasets

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