KEMET

KEMET identifies missing KEGG orthologs in microbial genomes using hidden Markov model profiles to improve annotation of metabolic reaction networks from metagenome-assembled genomes.

Key Features:

• KEGG integration: Leverages Kyoto Encyclopedia of Genes and Genomes (KEGG) functional units for metabolic reaction network analysis.
• Metabolic network analysis: Performs in-depth analysis of metabolic reaction networks to locate functional gaps.
• Hidden Markov model profiles: Uses hidden Markov model (HMM) profiles to detect missing orthologs, including KEGG Orthologs (KOs), within networks.
• Targeted ortholog search: Conducts targeted searches for orthologous sequences to fill annotation gaps and expand functional potential assessment.
• Assembly-impact simulation: Simulates the impact of assembly issues on real gene sequences to evaluate detection of KEGG orthologs missed by conventional tools.
• Draft genome gap-filling: Refines annotations in metagenome-assembled genomes (MAGs) to improve draft genome-scale metabolic models.
• Partial sequence handling: Addresses limitations caused by partial gene sequences and incomplete assemblies during annotation.
• Model accuracy improvement: Enhances the accuracy of genome-scale metabolic models toward levels comparable to models derived from complete genomes.

Scientific Applications:

• Microbial genome annotation: Improves annotation completeness of microbial genomes, particularly metagenome-assembled genomes (MAGs).
• Genome-scale metabolic model reconstruction: Enhances draft genome-scale metabolic models by filling KEGG ortholog gaps in metabolic networks.
• Functional potential assessment: Expands and refines assessments of metabolic functional potential of microbial organisms using KEGG-based annotations.
• Metabolic capability evaluation: Enables more precise qualitative and quantitative assessment of metabolic capabilities of novel microbial organisms.
• Annotation method evaluation: Evaluates detection performance of KEGG orthologs under simulated assembly issues to compare with conventional tools.
• Microbial ecology and metagenomics: Supports functional analyses in microbial ecology and metagenomic studies by improving annotations of incomplete genomes.

Methodology:

Performs in-depth analysis of metabolic reaction networks; employs hidden Markov model (HMM) profiles to identify missing orthologs (including KEGG Orthologs); conducts targeted searches for orthologous sequences to fill annotation gaps; and simulates the impact of assembly issues on real gene sequences to evaluate detection of KEGG orthologs.