CNBA: Coregulatory Network Builder and Analyzer

Abstract

Abstract Knowledge gained from coregulatory relationship studies can be used to develop drugs, modify treatment strategies, discover biomarkers, and so on. Proteins, RNAs, DNAs, transcription factors, and small molecules are commonly used to reveal the mechanisms of cellular systems in various contexts. Building and analyzing biomolecular networks helps in understanding complex biological systems. Although there are numerous tools for studying biological networks, tools for studying synergistic or coregulatory networks are limited. Therefore, we developed Coregulatory Network Builder and Analyzer (CNBA), a novel tool that uses network transformation (degree-preserving randomization) procedures based on prior bipartite relationship data among biomolecules to identify any correspondence between pairs of molecules under study. The tool identifies coregulatory or synergistic pairs and assigns each pair a coregulatory coefficient score. It performs an overrepresentation analysis on the pairs that have been identified as associated in order to add more biological context to the associated pairs identified. We have demonstrated the activities of the tool by a case study and described the advantages and limitations of it. CNBA's utility extends beyond computational biology, as it can be used in a variety of network analysis-based fields ranging from ecological research to social network analysis.