BiGG Models

BiGG Models hosts a centralized knowledgebase of high-quality genome-scale metabolic network reconstructions to provide standardized, validated genome-scale models linked to genome annotations and external databases for comparative and systems-level metabolic analysis across phylogenetic diversity.


Key Features:

  • Repository content: Contains gold-standard and multi-strain genome-scale metabolic network reconstructions.
  • Phylogenetic coverage: Includes models spanning the phylogenetic tree and has been expanded with 31 new models.
  • Genome annotation linking: Connects each model to relevant genome annotations and external databases.
  • Standardization: Ensures standardized representation of reactions and metabolites across models.
  • Multi-strain model support: Hosts multi-strain models intended for comparative analyses of related strains.
  • Quality validation: Employs Memote, a community-developed validator for genome-scale models.

Scientific Applications:

  • Comparative strain analysis: Enables comparison of related strains to investigate genetic and metabolic variation.
  • Metabolic network reconstruction and analysis: Supports reconstruction and analysis of genome-scale metabolic networks.
  • Systems biology and bioinformatics: Provides standardized, high-quality models for systems-level and bioinformatics investigations of metabolism.

Methodology:

Models are linked to genome annotations and external databases, reactions and metabolites are standardized across models, and model quality is assessed using Memote.

Topics

Details

Tool Type:
web application
Programming Languages:
JavaScript, Python
Added:
1/14/2020
Last Updated:
11/24/2024

Operations

Publications

Norsigian CJ, Pusarla N, McConn JL, Yurkovich JT, Dräger A, Palsson BO, King Z. BiGG Models 2020: multi-strain genome-scale models and expansion across the phylogenetic tree. Nucleic Acids Research. 2019. doi:10.1093/nar/gkz1054. PMID:31696234. PMCID:PMC7145653.

PMID: 31696234
PMCID: PMC7145653
Funding: - Novo Nordisk Fonden: NNF10CC1016517

Documentation

Links