GRNsight is an open source web application and service for visualizing models of small- to medium-scale gene regulatory networks. GRNsight is a joint project of the Loyola Marymount University Bioinformatics and Biomathematics Groups, headed by Dr. Kam Dahlquist, Dr. John David N. Dionisio, and Dr. Ben G. Fitzpatrick. Undergraduate students initiated the development of GRNsight in Spring 2014, including Britain Southwick (Computer Science, ’14) and Nicole Anguiano (Computer Science, ’16), with consultation from Katrina Sherbina (Biomathematics, ’14). For current contributors, please see our People page.
A gene regulatory network (GRN) consists of genes, transcription factors, and the regulatory connections between them, which govern the level of expression of mRNA and protein from those genes. GRNs can be mathematically modeled and simulated by applications such as GRNmap, a MATLAB program that estimates the parameters and performs forward simulations of a differential equations model of a GRN. Computer representations of GRNs, such as the models output by GRNmap, are in the form of a tabular spreadsheet (adjacency matrix) that is not easily interpretable. Ideally, GRNs should be displayed as diagrams (graphs) detailing the regulatory relationships (edges) between each gene (node) in the network. To address this need, we developed GRNsight.
Although originally designed for gene regulatory networks, we believe that GRNsight has general applicability for displaying any small, unweighted or weighted network with directed edges for systems biology or other application domains.
Most users will want to access GRNsight through the web application at http://dondi.github.io/GRNsight/. The source code is available for developers who wish to run their own instance of the GRNsight web service and/or web client.
If you use GRNsight in your work, please cite:
Dahlquist, K.D., Dionisio, J.D.N., Fitzpatrick, B.G., Anguiano, N.A., Varshneya, A., Southwick, B.J., Samdarshi, M. (2016) GRNsight: a web application and service for visualizing models of small- to medium-scale gene regulatory networks. PeerJ Computer Science 2:e85. DOI: 10.7717/peerj-cs.85.