Pseudomonas Genome Database

Pseudomonas Genome Database provides curated genomic data and analytical resources for comparative genomics and functional analysis across Pseudomonas species, with emphasis on Pseudomonas aeruginosa.

Key Features:

• Community-Based Annotation System: Community-driven genome annotation updates integrated with coordinator-conducted literature reviews.
• Boolean Search and Tab-Delimited Export: Boolean search capabilities over annotation and update-log databases with export of results as tab-delimited files.
• Integrated Tools and Analyses: Integration with PseudoCyc for pathway analysis, GBrowse for alternate and peer-reviewed annotation display, and information on knockout mutants.
• Genomic Insights into P. aeruginosa PAO1: Complete PAO1 sequence (6.3 million base pairs) showing a high proportion of regulatory genes and numerous genes involved in organic compound catabolism, transport, efflux, and chemotaxis related to intrinsic drug resistance.
• Polymorphism Analysis: Sequence polymorphism comparisons of PAO1 against other reference strains, incomplete genomes, and single gene sequences for phenotypic variation and population genomics studies.
• High-Precision Computational Predictions: High-precision predictions for protein subcellular localization and genomic islands linked to genome-scale experimental data and comparative genomics to identify essential genes and large-scale evolutionary events.
• Comparative Genomics Tools: Methods for predicting and clustering orthologs to identify core Pseudomonas genes and investigate evolutionary dynamics and ortholog/paralog relationships.

Scientific Applications:

• Comparative Genomics: Comparative analyses of genes, proteins, gene order, operons, and gene function categories across Pseudomonas species.
• Pathway and Metabolic Analysis: Pathway reconstruction and analysis using PseudoCyc to study metabolic capabilities.
• Functional Genomics: Integration of knockout mutant information with annotations to support gene function studies.
• Drug Resistance and Regulatory Network Studies: Investigation of intrinsic drug resistance and regulatory gene content in P. aeruginosa PAO1.
• Population Genomics and Phenotypic Variation: Analyses of sequence polymorphisms to assess phenotypic variation between closely related isolates and broader population structure.
• Essential Gene and Evolutionary Event Identification: Use of localization and genomic island predictions combined with comparative genomics to identify essential genes and large-scale evolutionary events.
• Core Genome Identification: Identification of core Pseudomonas genes through ortholog prediction and clustering.

Methodology:

Computational methods include Boolean searching of annotation and update-log databases; pathway analysis via PseudoCyc; GBrowse visualization of alternate and peer-reviewed annotations; computational predictions for protein subcellular localization and genomic islands; ortholog prediction and clustering; and sequence polymorphism comparisons.