The interactions between the components of a complex system define a network of connections consisting of nodes and edges. Complex systems often generate emergent properties (2) which are not present in an obvious way in its parts. The study of complex systems (1) is concerned with the question of how the relationships between the parts of a system give rise to its collective behavior. PerseusNet is extensible through a plugin architecture in a multi-lingual way, integrating analyses in C#, Python, and R, and is freely available at. Co-expression network analysis of proteomics data adopts established tools from transcriptome co-expression analysis. For posttranslational modifications of proteins, such as phosphorylation, a collection of dedicated network analysis tools helps in elucidating cellular signaling events. For affinity purification, a volcano-plot-based statistical analysis method for network generation is featured which is scalable to large numbers of baits. PerseusNet supports the biomedical researcher for both modes of data analysis with a multitude of activities. Proteomics is commonly used to generate networks, e.g., with affinity purification experiments, but networks are also used to explore proteomics data. We introduce PerseusNet, the new Perseus network module for the biological analysis of proteomics data. Proteomics data analysis strongly benefits from not studying single proteins in isolation but taking their multivariate interdependence into account.

Functional network analysis definition