Pseudomonas Genome Database

Pseudomonas Genome Database provides integrated genomic sequences and curated annotations for Pseudomonas species to support comparative genomics and functional analyses, with emphasis on Pseudomonas aeruginosa PAO1.

Key Features:

• Comprehensive Genomic Data: Contains complete genome sequences for Pseudomonas species, including P. aeruginosa PAO1, and reports sequence polymorphisms across strains.
• High-Quality Annotations: Maintains a community-driven annotation and curation process with reviewed updates integrated into genome annotations.
• Comparative Genomics Tools: Supports cross-strain and cross-species comparisons and ortholog assessment using a novel ortholog clustering method, with data views such as GBrowse and Mauve.
• Advanced Search Capabilities: Enables Boolean searches on annotations and sequences across genomes.
• Functional Predictions and Analyses: Provides predictions for protein subcellular localization and genomic islands, integrates PseudoCyc, and includes information on knockout mutants.
• Evolutionary Insights: Facilitates analysis of P. aeruginosa PAO1 regulatory gene content, metabolic capabilities, and genome features related to adaptability and resistance mechanisms.

Scientific Applications:

• Pathogenicity and Virulence Studies: Supports investigation of pathogenicity and virulence determinants in Pseudomonas species, notably P. aeruginosa.
• Antimicrobial Resistance Research: Enables analysis of genetic determinants and mechanisms of antimicrobial resistance.
• Metabolic and Regulatory Network Analysis: Facilitates assessment of metabolic capabilities and regulatory gene content, including integration with PseudoCyc.
• Comparative Genomics and Evolutionary Analysis: Enables genome-wide comparisons to identify orthologs, sequence polymorphisms, genomic islands, and evolutionary events.
• Functional Genomics and Essential Gene Identification: Supports identification of essential genes and interpretation of knockout mutant data.

Methodology:

Community-based annotation integrates computational predictions with experimental data and the resource employs robust ortholog prediction and clustering methods.