TY  - JOUR
A1  - Belcour, Arnaud
A1  - Got, Jeanne
A1  - Aite, Méziane
A1  - Delage, Ludovic
A1  - Collén, Jonas
A1  - Frioux, Clémence
A1  - Leblanc, Catherine
A1  - Dittami, Simon M.
A1  - Blanquart, Samuel
A1  - Markov, Gabriel V.
A1  - Siegel, Anne
T1  - Inferring and comparing metabolism across heterogeneous sets of annotated genomes using AuCoMe
Y1  - 2023/06/01 
JF  - Genome Research 
JO  - Genome Research 
SP  - 972 
EP  - 987 
DO  - 10.1101/gr.277056.122 
VL  - 33 
IS  - 6 
UR  - http://genome.cshlp.org/content/33/6/972.abstract 
N2  - 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 evolutionarily distant algae, underlining the potential of AuCoMe to accelerate the broad exploration of metabolic evolution across the tree of life. 
ER  -