GENPPI: standalone software for creating protein interaction networks from genomes

Area of Science:

• Genomics
• Bioinformatics
• Evolutionary Biology

Background:

• Increasing rate of bacterial genome deposition in databases.
• Genome annotation is crucial for understanding organisms and diseases.
• Existing methods lack standalone software for creating interaction networks using conserved gene neighborhoods (CNs), phylogenetic profiles (PPs), and gene fusions.

Purpose of the Study:

• To develop standalone software for ab initio prediction of protein interaction networks.
• To utilize CNs and PPs for enhanced genome analysis and evolutionary relationship determination.
• To provide an efficient and effective tool for analyzing large-scale genomic data.

Main Methods:

• Development of GENPPI software for ab initio prediction of interaction networks.
• Utilized conserved gene neighborhoods (CNs) and phylogenetic profiles (PPs) for network construction.
• Case study involved analysis of 50 genomes from the genus Corynebacterium.

Main Results:

• GENPPI successfully differentiated Corynebacterium pseudotuberculosis biovars based on PP relationships.
• Analysis using only CNs could separate species but not biovars.
• GENPPI efficiently generated interaction networks from 50 genomes in under 40 minutes.
• Predicted interaction networks accurately reflect evolutionary relationships, confirmed by average nucleotide identity analyses.
• Software allows user-defined parameterization for customized network creation (e.g., genus, metagenome, pangenome).

Conclusions:

• GENPPI addresses the gap in processing novel genomes for interaction network analysis.
• The software facilitates the study of non-core genes across multiple genome versions.
• Users can customize network generation based on the number and evolutionary correlation of genomes studied.