AuCoMe: inferring and comparing metabolisms across heterogeneous sets of annotated genomes
FRIOUX, Clémence
Pleiade, from patterns to models in computational biodiversity and biotechnology [PLEIADE]
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Pleiade, from patterns to models in computational biodiversity and biotechnology [PLEIADE]
Language
en
Document de travail - Pré-publication
English Abstract
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 ...Read more >
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.Read less <
English Keywords
Bioinformatics
Proteins genomics
metabolism
metabolic evolution
genomes
systems biology
Origin
Hal imported