|Title||SyNDI : Synchronous network data integration framework|
|Author(s)||Lindfors, Erno; Dam, Jesse C.J. van; Lam, Carolyn Ming Chi; Zondervan, Niels A.; Martins dos Santos, Vitor A.P.; Suarez-Diez, Maria|
|Source||BMC Bioinformatics 19 (2018)1. - ISSN 1471-2105|
Systems and Synthetic Biology
|Publication type||Refereed Article in a scientific journal|
|Keyword(s)||Cytoscape - Galaxy - Mycobacterium tuberculosis - Network biology - Staphylococcus aureus - Synchronous network visualization - Systems biology - Workflow|
Background: Systems biology takes a holistic approach by handling biomolecules and their interactions as big systems. Network based approach has emerged as a natural way to model these systems with the idea of representing biomolecules as nodes and their interactions as edges. Very often the input data come from various sorts of omics analyses. Those resulting networks sometimes describe a wide range of aspects, for example different experiment conditions, species, tissue types, stimulating factors, mutants, or simply distinct interaction features of the same network produced by different algorithms. For these scenarios, synchronous visualization of more than one distinct network is an excellent mean to explore all the relevant networks efficiently. In addition, complementary analysis methods are needed and they should work in a workflow manner in order to gain maximal biological insights. Results: In order to address the aforementioned needs, we have developed a Synchronous Network Data Integration (SyNDI) framework. This framework contains SyncVis, a Cytoscape application for user-friendly synchronous and simultaneous visualization of multiple biological networks, and it is seamlessly integrated with other bioinformatics tools via the Galaxy platform. We demonstrated the functionality and usability of the framework with three biological examples - we analyzed the distinct connectivity of plasma metabolites in networks associated with high or low latent cardiovascular disease risk; deeper insights were obtained from a few similar inflammatory response pathways in Staphylococcus aureus infection common to human and mouse; and regulatory motifs which have not been reported associated with transcriptional adaptations of Mycobacterium tuberculosis were identified. Conclusions: Our SyNDI framework couples synchronous network visualization seamlessly with additional bioinformatics tools. The user can easily tailor the framework for his/her needs by adding new tools and datasets to the Galaxy platform.