csppinet: A Python package for context-specific biological network construction and analysis based on omics data

Abstract

AbstractThe analysis of large-scale biological data, particularly omics data, has become essential for understanding complex biological systems. In this study, we present a Python package for the construction of context-specific biological networks based on gene expression activity and protein-protein interaction (PPI) data. The package leverages computational tools and the NetworkX library for network analysis. Through a case study focusing on yeast fermentation with glucose and xylose as carbon sources, we demonstrate the package’s capabilities. The context-specific networks derived from these fermentation conditions were compared to highlight the impact of different carbon sources and stages on network dynamics. Hub genes were identified within each network, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to understand their functional implications. The results revealed distinct hub genes and enriched biological processes in each context-specific network. In the glucose-specific network, the exclusively proteins in this network revealed enrichment in terms related tochromosome organization, biological regulation, MAPK signaling pathwayandmismatch repair pathway. Conversely, the exclusively proteins in xylose-specific network revealed enrichment ingeneration of precursor metabolites and energy), mitochondrion organization, response to extracellular stimulus, glutamate metabolic process, cellular response to alcohol, beta-Alanine metabolism, arginine and proline metabolism, glyoxylate and dicarboxylate metabolismandpyruvate metabolism. The developed Python package and context-specific networks provide researchers with a valuable framework to explore complex biological phenomena. By integrating gene expression profiles and PPI data, researchers can gain insights into context-dependent molecular interactions and regulatory mechanisms. These findings contribute to the understanding of cellular behavior and have potential implications in disease mechanisms, biomarker identification, and drug target discovery. The csppinet package is available athttps://github.com/lmigueel/csppinet.