Optimal Approximation of the Pangenomic Gene Spectrum Function

In recent years, the study of bacterial pangenomes has significantly enhanced the understanding of genomic diversity and evolutionary processes occurring in microbiomes. The pangenome includes all genes found in all strains of a given bacterial species and is divided into three groups: core, shell, and cloud. The aim of this work is to develop an algorithm to accurately approximate the pangenomic gene spectrum function and study its relationship with various ecological, taxonomic and genomic characteristics of bacteria. An algorithm was developed to perform the study, which involved processing the raw genomic data and approximating the pangenomic function to evaluate its structure and patterns. The algorithm was tested on a set of 54 bacterial species, including both generalists adapted to a wide range of ecological niches and specialists representing highly specialised species. This allowed us to evaluate the versatility and efficiency of the algorithm at different levels of genomic variability and microbial diversification. The results of the tests demonstrated that for the majority of species (approximately 80%), the approximation by the sum of three exponents was optimal. In generalists, the stepped form was observed to be 1.5-2 times more prevalent than in specialists, indicating that the preferred approximation was related to the ecological characteristics of the bacteria. An important aspect of the study was the identification of correlations between the spectrum of pangenomic genes and ecological, taxonomic and genomic characteristics of bacteria. The results demonstrated that in bacteria adapted to life in multiple ecological niches, the approximation of the pangenomic gene spectrum function by a step analytic form is more prevalent than in highly specialized species. This indicates a higher degree of genetic variability and adaptability in generalists. The results of this study contribute to the expansion of our knowledge of the pangenomic characteristics of bacteria and emphasise the importance of further research in bioinformatics and genomics for a more complete understanding of microbial life. The findings can be used to develop new methods to analyse genomic data and improve our understanding of the evolutionary processes occurring in microbiomes.