Enabling comparative modeling of closely related genomes: example genus Brucella

Area of Science:

• Microbial genomics
• Systems biology
• Bioinformatics

Background:

• Comparing metabolic models of closely related genomes is essential for scientific applications.
• Publicly available annotated genomes often contain inconsistencies, complicating metabolic model reconstruction.
• Automated reconstruction methods may miss crucial biochemical reactions.

Observation:

• A protocol for comparative analysis of metabolic models was developed using fifteen Brucella strains.
• Annotation inconsistencies in the SEED database were identified and corrected.
• Thirty-one common biochemical reactions in Brucella were newly identified.

Findings:

• The developed protocol effectively identifies and corrects annotation inconsistencies in genomic databases.
• Comparative analysis revealed previously unidentified biochemical reactions common to Brucella.
• Improvements have been integrated into major bioinformatics platforms like PATRIC, Model-SEED, KBase, and RAST.

Implications:

• This protocol enables the creation of consistent annotation systems and high-quality metabolic models.
• Accurate metabolic models will improve predictions of bacterial phenotypes, including pathogenicity and metabolic capabilities.
• Enhances comparative genomics and systems biology research for microbial pathogens.