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

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 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 evol

SUBMITTER: Belcour A 

PROVIDER: S-EPMC10629481 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature