A large-scale phylogeny-guided analysis of pseudogenes in Pseudomonas aeruginosa bacterium

Pseudogenes, once considered junk DNA based on the incorrect assumption that the absence of full coding potential means a complete lack of functionality, have recently become a subject of significantsignificant interest in the scientificscientific community. Concurrently, it is widely assumed that bacterial genomes are compact and have a high density of coding genes with little room for non-coding genes, including pseudogenes. A key aspect of genome annotation is the correct identificationidentification of genes and the distinction between coding genes and pseudogenes, as it directly impacts functional and comparative genomics studies. In this study, we analyzed the genomic data of 4,699 strains of the bacterium Pseudomonas aeruginosa (P. aeruginosa) as they exhibit high variability in the number of annotated pseudogenes. In particular, we looked for correlations between the number of pseudogenes and other genomic and metafeatures of the strains. We identifiedidentified clusters of orthologous genes and pseudogenes and compared cluster size distributions and length homogeneity within clusters. We then mapped and examined orthology relationships between genes and pseudogenes. Additionally, we generated a phylogenetic tree of the strains and found that phylogenetically related strains are more homogeneous in the number of pseudogenes and share a significantsignificant amount of pseudogenes. Finally, we delved into clusters of orthologous genes and pseudogenes and quantifiedquantified their phylogenetic neighborhood, classifying pseudogenes into evolutionary preserved pseudogenes, mis-annotated pseudogenes, or pseudogenes formed by failed horizontal transfer events. This in-depth study provides important insights that can be incorporated into pseudogene annotation pipelines in the future.

Automated summary

Pseudogenes, once considered junk DNA, have recently gained significant interest in the scientific community. In this study, we analyzed the genomic data of Pseudomonas aeruginosa strains and identified correlations between pseudogenes and other genomic features. We also discovered clusters of orthologous genes and pseudogenes, and found that related strains share a significant amount of pseudogenes. Additionally, we classified pseudogenes into different types based on their phylogenetic neighborhood. This in-depth study provides important insights for future pseudogene annotation pipelines.