Abstract
A large-scale molecular interaction network of protein-protein interactions (PPIs) enables the automatic detection of molecular functional modules through a computational approach. However, the functional modules that are typically detected by topological community detection algorithms may be diverse in functional homogeneity and are empirically considered to be default functional modules. Thus, a significant challenge that has been described but not elucidated is investigating the relationship between topological modules and functional modules. We systematically investigated this issue by initially using seven widely used community detection algorithms to partition the PPI network into communities. Four homogeneity measures were subsequently implemented to evaluate the functional homogeneity of protein community. We determined that a significant portion of topological modules with heterogeneous functionality exists and should be further investigated; moreover, these findings indicated that topologically based functional module detection approaches must be reconsidered. Furthermore, we found that the functional homogeneity of topological modules is positively correlated with their edge densities, degree of association with diseases and general Gene Ontology (GO) terms. Thus, topologically based module detection approaches should be used with caution in the identification of functional modules with high homogeneity
Highlights
Cellular functions are mostly conducted in a highly modular manner[1] in the context of a molecular interaction network[2] whose underlying universal laws may potentially be elucidated by advanced approaches derived from network biology[3]
We investigated the underlying modular structure in the human protein-protein interaction network derived from STRING9 by adopting seven well-studied community detection algorithms (BGLL, Incremental BGLL (IBGLL), Newman Spectral (NS), Label Propagation (RAK), Walktrap (WT), Link Community (LC) and ClusterONE (CO); see the Materials and Methods section)
Most biological functions arise from interactions among many molecular components, which typically form functionally related modules to exert their activities[3,16,34], The identification of functional modules is a critical process for understanding the potential mechanism of molecular interactions within cells and the underlying mechanisms of complicated disease phenotypes[4,35], the availability of various types of large-scale interactome networks[36], such as protein-protein interactions (PPIs), signal transduction networks and metabolic networks, have paved the way for the prediction of biological functions using network-based approaches[8,24]
Summary
Cellular functions are mostly conducted in a highly modular manner[1] in the context of a molecular interaction network[2] whose underlying universal laws may potentially be elucidated by advanced approaches derived from network biology[3]. The disease module[6,12], a particular neighborhood with tightly linked proteins associated with a specific phenotype, may be identified from the PPI network through topological network analysis. Spirin and Mirny[16] have applied three methods for group identification in the PPI network and have subsequently shown that these topological clusters correspond to protein complexes and functional modules. A graph entropy approach for the identification of functional modules from the PPI network has been proposed by Kenley et al.[18]. These previously described methods have generated functional modules from topological modules; it is assumed that topological, functional and disease modules overlap. We analyzed two causes of functional diversity of the modules: disease-related genes and GO term levels
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
