I've started this issue to let you all know that I'm submitting an abstract for the Yeast Genetics Meeting to be held at UCLA, August 17-21, 2022.
Integration of SGD Regulatory and Expression Data into the GRNmap and GRNsight Applications for Modeling and Visualizing Small-to-Medium Gene Regulatory Networks
Kam D. Dahlquist, Onariaginosa O. Igbinedion, Ahmad R. Mersaghian, Sarron A. Tadesse, John David N. Dionisio
Department of Biology, Department of Computer Science, Loyola Marymount University, 1 LMU Drive, Los Angeles, CA 90045 USA
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 genes. GRNmap is an open source MATLAB program that performs parameter estimation and forward simulation of a differential equations model of a GRN based on time course gene expression data. GRNsight is an open source web application for visualizing small-to-medium scale GRNs, especially models produced by GRNmap. GRNsight reads the Excel input and output workbooks from GRNmap and automatically displays the model data as a graph with colored nodes (expression data) and edges (estimated regulatory weights). A limitation for GRNmap has been the manual creation of input Excel workbooks which is time-consuming, error-prone, and dependent upon the user having their own network and expression data. To address this, we have implemented a backend PostgreSQL database for GRNsight, populated with five public gene expression datasets (Apweiler et al. 2012, GSE33098; Barreto et al. 2012, GSE24712; Dahlquist et al. 2018, GSE83656; Kitagawa et al. 2002, GSE9336; Thorsen et al. 2007, GSE6068) and regulatory network data from the Saccharomyces Genome Database (SGD). A user can now select genes to include in the GRN, and GRNsight will automatically layout the network using regulatory connections from SGD. The user can then select one of the time course gene expression datasets with which to color the nodes and export to a properly-formatted GRNmap input workbook. GRNmap can then be used to estimate the regulatory weights (activation vs. repression and magnitude of the relationship). A GRNmap executable is available for users who do not have access to a MATLAB license. Finally, the GRNmap output can be loaded back into GRNsight to visualize the results. Closing this loop for automating and validating the creation of GRNmap input workbooks speeds up the rate of research, enabling the comparison of models of different GRNs with the same expression data source or the same GRN with different expression data sources. GRNmap is available at http://kdahlquist.github.io/GRNmap/; GRNsight is available at http://dondi.github.io/GRNsight.
I've started this issue to let you all know that I'm submitting an abstract for the Yeast Genetics Meeting to be held at UCLA, August 17-21, 2022.
Integration of SGD Regulatory and Expression Data into the GRNmap and GRNsight Applications for Modeling and Visualizing Small-to-Medium Gene Regulatory Networks
Kam D. Dahlquist, Onariaginosa O. Igbinedion, Ahmad R. Mersaghian, Sarron A. Tadesse, John David N. Dionisio Department of Biology, Department of Computer Science, Loyola Marymount University, 1 LMU Drive, Los Angeles, CA 90045 USA
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 genes. GRNmap is an open source MATLAB program that performs parameter estimation and forward simulation of a differential equations model of a GRN based on time course gene expression data. GRNsight is an open source web application for visualizing small-to-medium scale GRNs, especially models produced by GRNmap. GRNsight reads the Excel input and output workbooks from GRNmap and automatically displays the model data as a graph with colored nodes (expression data) and edges (estimated regulatory weights). A limitation for GRNmap has been the manual creation of input Excel workbooks which is time-consuming, error-prone, and dependent upon the user having their own network and expression data. To address this, we have implemented a backend PostgreSQL database for GRNsight, populated with five public gene expression datasets (Apweiler et al. 2012, GSE33098; Barreto et al. 2012, GSE24712; Dahlquist et al. 2018, GSE83656; Kitagawa et al. 2002, GSE9336; Thorsen et al. 2007, GSE6068) and regulatory network data from the Saccharomyces Genome Database (SGD). A user can now select genes to include in the GRN, and GRNsight will automatically layout the network using regulatory connections from SGD. The user can then select one of the time course gene expression datasets with which to color the nodes and export to a properly-formatted GRNmap input workbook. GRNmap can then be used to estimate the regulatory weights (activation vs. repression and magnitude of the relationship). A GRNmap executable is available for users who do not have access to a MATLAB license. Finally, the GRNmap output can be loaded back into GRNsight to visualize the results. Closing this loop for automating and validating the creation of GRNmap input workbooks speeds up the rate of research, enabling the comparison of models of different GRNs with the same expression data source or the same GRN with different expression data sources. GRNmap is available at http://kdahlquist.github.io/GRNmap/; GRNsight is available at http://dondi.github.io/GRNsight.