ArticleMethods in molecular biology (Clifton, N.J.)2024
pyPGCF: A Python Software for Phylogenomic Analysis, Species Demarcation, Identification of Core, and Fingerprint Proteins of Bacterial Genomes That Are Important for Plants.
Article in Methods in molecular biology (Clifton, N.J.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Advances and challenges in understanding evolution through genome comparison: meeting report of the European Molecular Biology Organization (EMBO) lecture course "Evolutionary and Comparative Genomics".Bioinformatics advances · 2025Article
- Large genomic, functional, and phenotypical diversity ofFEMS microbes · 2025Article
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Abstract
This computational protocol describes how to use pyPGCF, a python software package that runs in the linux environment, in order to analyze bacterial genomes and perform: (i) phylogenomic analysis, (ii) species demarcation, (iii) identification of the core proteins of a bacterial genus and its individual species, (iv) identification of species-specific fingerprint proteins that are found in all strains of a species and, at the same time, are absent from all other species of the genus, (v) functional annotation of the core and fingerprint proteins with eggNOG, and (vi) identification of secondary metabolite biosynthetic gene clusters (smBGCs) with antiSMASH. This software has already been implemented to analyze bacterial genera and species that are important for plants (e.g., Pseudomonas, Bacillus, Streptomyces). In addition, we provide a test dataset and example commands showing how to analyze 165 genomes from 55 species of the genus Bacillus. The main advantages of pyPGCF are that: (i) it uses adjustable orthology cut-offs, (ii) it identifies species-specific fingerprints, and (iii) its computational cost scales linearly with the number of genomes being analyzed. Therefore, pyPGCF is able to deal with a very large number of bacterial genomes, in reasonable timescales, using widely available levels of computing power.
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