BELCOUR, Arnaud; GOT, Jeanne; AITE, Méziane; DELAGE, Ludovic; COLLEN, Jonas; FRIOUX, Clémence; LEBLANC, Catherine; DITTAMI, Simon; BLANQUART, Samuel; MARKOV, Gabriel; SIEGEL, Anne

doi:10.1101/2022.06.14.496215

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BELCOUR, Arnaud
Dynamics, Logics and Inference for biological Systems and Sequences [Dyliss]

GOT, Jeanne
Dynamics, Logics and Inference for biological Systems and Sequences [Dyliss]

AITE, Méziane
Dynamics, Logics and Inference for biological Systems and Sequences [Dyliss]

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Comparative analysis of Genome-Scale Metabolic Networks (GSMNs) may yield important information on the biology, evolution, and adaptation of species. However, it is impeded by the high heterogeneity of the quality and completeness of structural and functional genome annotations, which may bias the results of such comparisons. To address this issue, we developed AuCoMe-a pipeline to automatically reconstruct homogeneous GSMNs from a heterogeneous set of annotated genomes without discarding available manual annotations. We tested AuCoMe with three datasets, one bacterial, one fungal, and one algal, and demonstrated that it successfully reduces technical biases while capturing the metabolic specificities of each organism. Our results also point out shared metabolic traits and divergence points among evolutionarily distant species, such as algae, underlining the potential of AuCoMe to accelerate the broad exploration of metabolic evolution across the tree of life.< Réduire

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Bioinformatics

Proteins genomics

metabolism

metabolic evolution

genomes

systems biology

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Inferring and comparing metabolism across heterogeneous sets of annotated genomes using AuCoMe

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Résumé en anglais

Mots clés en anglais

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