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

hal.structure.identifierDynamics, Logics and Inference for biological Systems and Sequences [Dyliss]
dc.contributor.authorBELCOUR, Arnaud
hal.structure.identifierDynamics, Logics and Inference for biological Systems and Sequences [Dyliss]
dc.contributor.authorGOT, Jeanne
hal.structure.identifierDynamics, Logics and Inference for biological Systems and Sequences [Dyliss]
dc.contributor.authorAITE, Méziane
hal.structure.identifierLaboratoire de Biologie Intégrative des Modèles Marins [LBI2M]
dc.contributor.authorDELAGE, Ludovic
hal.structure.identifierLaboratoire de Biologie Intégrative des Modèles Marins [LBI2M]
dc.contributor.authorCOLLÉN, Jonas
hal.structure.identifierPleiade, from patterns to models in computational biodiversity and biotechnology [PLEIADE]
dc.contributor.authorFRIOUX, Clémence
hal.structure.identifierLaboratoire de Biologie Intégrative des Modèles Marins [LBI2M]
dc.contributor.authorLEBLANC, Catherine
hal.structure.identifierLaboratoire de Biologie Intégrative des Modèles Marins [LBI2M]
dc.contributor.authorDITTAMI, Simon
hal.structure.identifierDynamics, Logics and Inference for biological Systems and Sequences [Dyliss]
dc.contributor.authorBLANQUART, Samuel
hal.structure.identifierLaboratoire de Biologie Intégrative des Modèles Marins [LBI2M]
dc.contributor.authorMARKOV, Gabriel
hal.structure.identifierDynamics, Logics and Inference for biological Systems and Sequences [Dyliss]
dc.contributor.authorSIEGEL, Anne
dc.date.issued2023-06
dc.identifier.issn1088-9051
dc.description.abstractEnComparative 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 data sets, one bacterial, one fungal, and one algal, and showed that it successfully reduces technical biases while capturing the metabolic specificities of each organism. Our results also point out shared and divergent metabolic traits among evolutionarily distant algae, underlining the potential of AuCoMe to accelerate the broad exploration of metabolic evolution across the tree of life.
dc.language.isoen
dc.publisherCold Spring Harbor Laboratory Press
dc.rights.urihttp://creativecommons.org/licenses/by/
dc.subject.engenomics
dc.subject.enmetabolism
dc.subject.enmetabolic evolution
dc.subject.engenomes
dc.subject.ensystems biology
dc.title.enInferring and comparing metabolism across heterogeneous sets of annotated genomes using AuCoMe
dc.typeArticle de revue
dc.identifier.doi10.1101/gr.277056.122
dc.subject.halSciences du Vivant [q-bio]/Bio-Informatique, Biologie Systémique [q-bio.QM]
dc.subject.halSciences du Vivant [q-bio]/Biochimie, Biologie Moléculaire/Génomique, Transcriptomique et Protéomique [q-bio.GN]
bordeaux.journalGenome Research
bordeaux.page972 - 987
bordeaux.volume33
bordeaux.peerReviewedoui
hal.identifierhal-04192851
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-04192851v1
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