gapseq

gapseq predicts bacterial metabolic pathways and reconstructs genome-scale metabolic networks from genomic sequences to infer metabolic capabilities and phenotypes.

Key Features:

• Integration with databases: Leverages UniProt, TCDB, MetaCyc, KEGG and ModelSEED for sequence-homology and pathway-topology based analyses.
• Automated pathway prediction: Automates prediction of an organism's metabolic pathways from genomic data using curated database information.
• LP-based gap-filling algorithm: Implements a linear programming (LP)-based gap-filling algorithm to complete genome-scale metabolic models.
• Constraint-based modeling compatibility: Produces reconstructions suitable for constraint-based flux analysis to predict metabolic phenotypes.
• Validation and accuracy: Validated against experimental data from over 3,000 bacterial organisms covering 14,895 phenotypic traits (enzyme activity, energy sources, fermentation products, gene essentiality) with an overall accuracy of 81%.
• Multi-species community modeling: Reconstructed models can be used to simulate biochemical interactions within multi-species microbial communities.

Scientific Applications:

• Biotechnological research: Predicts metabolic capabilities to support microbial engineering for industrial processes.
• Ecological studies: Models multi-species interactions and community metabolism for ecosystem and microbiome research.
• Medical research: Provides metabolic insights into bacterial phenotypes relevant to host–microbe interactions and therapeutic strategy development.

Methodology:

Performs sequence-homology and pathway-topology analyses using UniProt, TCDB, MetaCyc, KEGG and ModelSEED and applies an LP-based gap-filling algorithm to reconstruct genome-scale metabolic models suitable for constraint-based flux analysis.