PseudoCAP pseudomonas genome database

PseudoCAP pseudomonas genome database provides integrated, curated Pseudomonas genome sequences and computational analyses to support comparative genomics, sequence polymorphism analysis, identification of putative essential and core genes, and genomic island prediction.

Key Features:

• Integrated Genome Sequences: Consolidates completely sequenced Pseudomonas genomes with detailed annotations for strain-level genomic analyses.
• Sequence Polymorphism Analysis: Enables viewing of sequence polymorphisms in Pseudomonas aeruginosa PAO1 compared to other reference strains, incomplete genomes, and single gene sequences.
• High-Precision Computational Predictions: Provides strain-specific, updated predictions including refined protein subcellular localization and genomic island predictions.
• Comparative Genomics Tools: Includes genus-tailored methods for predicting and clustering orthologs to support evolutionary analyses and core gene identification.
• Linkage to Experimental Data: Links genome-scale computational analyses with experimental data to support validation of predictions.
• Identification of Essential Genes and Evolutionary Events: Supports identification of putative essential genes, core Pseudomonas genes, and large-scale evolutionary events.

Scientific Applications:

• Comparative Genomics: Comparative analysis of annotations and sequences across Pseudomonas strains to identify conserved and variable genomic features.
• Phenotypic Variation Analysis: Analysis of sequence polymorphisms to investigate genetic bases of phenotypic differences among isolates.
• Evolutionary Studies: Detection of large-scale evolutionary events and ortholog clustering to study Pseudomonas evolution and adaptation.
• Essential Gene Identification: Identification of putative essential and core Pseudomonas genes for functional genomics and experimental targeting.
• Method Transfer to Other Genera: Adaptation of the database's comparative and annotation approaches for genus-level research beyond Pseudomonas.

Methodology:

Computational methods explicitly include strain-specific computational predictions, protein subcellular localization prediction, genomic island prediction, ortholog prediction and clustering, sequence polymorphism analysis, and linking genome-scale computational analyses with experimental data.