AuCoMe
AuCoMe reconstructs and standardizes genome-scale metabolic networks (GSMNs) from annotated genomes to enable unbiased comparative metabolic analysis across organisms.
Key Features:
- Automated GSMN Reconstruction: Automatically reconstructs genome-scale metabolic networks from heterogeneous genome annotations while integrating existing manual annotations.
- Network Homogenization: Standardizes metabolic network structure and annotations across organisms to reduce technical biases in comparative analyses.
- Metabolic Specificity Preservation: Maintains organism-specific metabolic characteristics while producing comparable GSMN representations.
- Cross-Species Comparative Analysis: Enables identification of shared and divergent metabolic features among different organisms.
Scientific Applications:
- Comparative Metabolic Network Analysis: Facilitates systematic comparison of genome-scale metabolic networks across multiple species.
- Metabolic Evolution Research: Supports investigation of conserved and divergent metabolic pathways across evolutionary lineages.
- Microbial and Eukaryotic Metabolism Studies: Enables metabolic network analysis across diverse taxa including bacteria, fungi, and algae.
Methodology:
AuCoMe processes annotated genomes to automatically reconstruct genome-scale metabolic networks while integrating manual annotations and standardizing network structure for comparative analysis.
Topics
Details
- License:
- GPL-3.0
- Cost:
- Free of charge
- Tool Type:
- library, workflow
- Programming Languages:
- Python
- Added:
- 6/19/2024
- Last Updated:
- 11/24/2024
Operations
Publications
Belcour A, Got J, Aite M, Delage L, Collén J, Frioux C, Leblanc C, Dittami SM, Blanquart S, Markov GV, Siegel A. Inferring and comparing metabolism across heterogeneous sets of annotated genomes using AuCoMe. Genome Research. 2023;33(6):972-987. doi:10.1101/gr.277056.122. PMID:37468308. PMCID:PMC10629481.
PMID: 37468308
PMCID: PMC10629481
Funding: - National Research Agency: ANR-10-BTBR-04
- Région Bretagne: SAD 2016-METALG (9673)