Name: inherent dynamics visualizer, an interactive application for evaluating and visualizing outputs from a gene regulatory network inference pipeline
Uploaded: 2021-12-07T13:30:00
Description: Watch this Scientific Journal Video about Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline at JoVE.com

This work was funded by the NIH grant R01 GM126555-01 and NSF grant DMS-1839299.

1. Install the IDP and IDV
NOTE: This section assumes that docker, conda, pip, and git are installed already (Table of materials).
<ol>
	<li>In a terminal, enter the command: git clone&#160;https://gitlab.com/biochron/inherent_dynamics_pipeline.git.</li>
	<li>Follow the install instructions in the IDP&#39;s README file.</li>
	<li>In a terminal, enter the command: git clone&#160;https://gitlab.com/bertfordley/inherent_dynamics_visualizer.git.</em...

<ol>
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(including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). , 300-308 (2017).</li><li>Cummins, B., Gedeon, T., Harker, S., Mischaikow, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=DSGRN:+Examining+the+dynamics+of+families+of+logical+models.">DSGRN: Examining the dynamics of families of logical models.</a> Frontiers in Physiology. 9. 9, 549 (2018).</li><li>. DSGRN Available from: <a target="_blank" href="https://github.com/marciogameiro/DSGRN">https://github.com/marciogameiro/DSGRN</a> (2021)</li><li>. Dsgm_Net_Gen Available from: <a target="_blank" href="https://github.com/breecummins/dsgrn_net_gen">https://github.com/breecummins/dsgrn_net_gen</a> (2021)</li><li>. Dsgrn_Net_Query Available from: <a target="_blank" href="https://github.com/breecummins/dsgrn_net_query">https://github.com/breecummins/dsgrn_net_query</a> (2021)</li><li>Orlando, D. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Global+control+of+cell-cycle+transcription+by+coupled+CDK+and+network+oscillators.">Global control of cell-cycle transcription by coupled CDK and network oscillators.</a> Nature. 453 (7197), 944-947 (2008).</li><li>Monteiro, P. T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=YEASTRACT+:+a+portal+for+cross-species+comparative+genomics+of+transcription+regulation+in+yeasts.">YEASTRACT+: a portal for cross-species comparative genomics of transcription regulation in yeasts.</a> Nucleic Acids Research. 48 (1), 642-649 (2020).</li><li>de Bruin, R. A. M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Constraining+G1-specific+transcription+to+late+G1+phase:+The+MBF-associated+corepressor+Nrm1+acts+via+negative+feedback.">Constraining G1-specific transcription to late G1 phase: The MBF-associated corepressor Nrm1 acts via negative feedback.</a> Molecular Cell. 23 (4), 483-496 (2006).</li><li>Horak, C. E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Complex+transcriptional+circuitry+at+the+G1/S+transition+in+Saccharomyces+cerevisiae.">Complex transcriptional circuitry at the G1/S transition in Saccharomyces cerevisiae.</a> Genes &amp; Development. 16 (23), 3017-3033 (2002).</li><li>Cherry, J. M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Saccharomyces+genome+database:+The+genomics+resource+of+budding+yeast.">Saccharomyces genome database: The genomics resource of budding yeast.</a> Nucleic Acids Research. 40, 700-705 (2012).</li><li>Zhu, G., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Two+yeast+forkhead+genes+regulate+the+cell+cycle+and+pseudohyphal+growth.">Two yeast forkhead genes regulate the cell cycle and pseudohyphal growth.</a> Nature. 406 (6791), 90-94 (2000).</li><li>Loy, C. J., Lydall, D., Surana, U. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=NDD1,+a+high-dosage+suppressor+of+cdc28-1N,+is+essential+for+expression+of+a+subset+of+late-S-phase-specific+genes+in+saccharomyces+cerevisiae.">NDD1, a high-dosage suppressor of cdc28-1N, is essential for expression of a subset of late-S-phase-specific genes in saccharomyces cerevisiae.</a> Molecular and Cellular Biology. 19 (5), 3312-3327 (1999).</li><li>Cho, C. Y., Kelliher, C. M., Hasse, S. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+cell-cycle+transcriptional+network+generates+and+transmits+a+pulse+of+transcription+once+each+cell+cycle.">The cell-cycle transcriptional network generates and transmits a pulse of transcription once each cell cycle.</a> Cell Cycle. 18 (4), 363-378 (2019).</li><li>Smith, L. 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The inference of GRNs is an important challenge in systems biology. The IDP generates model GRNs from gene expression data using a sequence of tools that utilize the data in increasingly complex ways. Each step requires decisions about how to process the data and what elements (genes, functional interactions) will be passed to the next layer of the IDP. The impacts of these decisions on IDP results are not as obvious. To help in this regard, the IDV provides useful interactive visualizations of the outputs from individua...

Many important biological processes, such as cell differentiation and environmental response, are governed by sets of genes that interact with each other in a gene regulatory network (GRN). These GRNs produce the transcriptional dynamics needed for activating and maintaining the phenotype they control, so identifying the components and topological structure of the GRN is key to understanding many biological processes and functions. A GRN may be modeled as a set of interacting genes and/or gene products described by a network whose nodes are the genes and whose edges describe the direction and form of interaction (e.g., activation/repression of transcription, post-translational modification, etc.)1. Interactions can then be expressed as parameterized mathematical models describing the impact a regulating gene has on the production of its target(s)2,3,4. Inference of a GRN model requires both an inference of the structure of the interaction network and estimation of the underlying interaction parameters. A variety of computational inference methods have been developed that ingest time series gene expression data and output GRN models5. Recently, a new GRN inference method was developed, called the Inherent Dynamics Pipeline (IDP), that utilizes time series gene expression data to produce GRN models with labeled regulator-target interactions that are capable of producing dynamics that match the observed dynamics in the gene expression data6. The IDP is a suite of tools connected linearly into a pipeline and can be broken down into three steps: a Node Finding step that ranks genes based on gene expression characteristics known or suspected to be related to the function of the GRN7,8, an Edge Finding step that ranks pairwise regulatory relationships8,9, and a Network Finding step that produces GRN models that are capable of producing the observed dynamics10,11,12,13,14,15.
Like most computational methods, the IDP requires a set of user-specified arguments that dictate how the input data is analyzed, and different sets of arguments can produce different results on the same data. For example, several methods, including the IDP, contain arguments that apply some threshold on the data, and increasing/decreasing this threshold between successive runs of the particular method can result in dissimilar results between runs (see Supplement Note 10: Network inference methods of5). Understanding how each argument may impact the analysis and subsequent results is important for achieving high confidence in the results. Unlike most GRN inference methods, the IDP consists of multiple computational tools, each having its own set of arguments that a user must specify and each having its own results. While the IDP provides extensive documentation on how to parameterize each tool, the interdependency of each tool on the output of the previous step makes parameterizing the entire pipeline without intermediate analyses challenging. For instance, arguments in the Edge and Network Finding steps are likely to be informed by prior biological knowledge, and so will depend on the dataset and/or organism. To interrogate intermediate results, a basic understanding of programming, as well as a deep understanding of all the result files and their contents from the IDP, would be needed.
The Inherent Dynamics Visualizer (IDV) is an interactive visualization package that runs in a user&#39;s browser window and provides a way for users of the IDP to assess the impact of their argument choices on results from any step in the IDP. The IDV navigates a complicated directory structure produced by the IDP and gathers the necessary data for each step and presents the data in intuitive and interactive figures and tables for the user to explore. After exploring these interactive displays, the user can produce new data from an IDP step that can be based on more informed decisions. These new data can then be immediately used in the next respective step of the IDP. Additionally, exploration of the data can help determine whether an IDP step should be rerun with adjusted parameters. The IDV can enhance the use of the IDP, as well as make the use of the IDP more intuitive and approachable, as demonstrated by investigating the core oscillator GRN of the yeast cell-cycle. The following protocol includes IDP results from a fully parameterized IDP run versus an approach that incorporates the IDV after runs of each IDP step, i.e., Node, Edge, and Network Finding.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Docker</td>
			<td></td>
			<td></td>
			<td>https://docs.docker.com/get-docker/</td>
		</tr>
		<tr>
			<td>Git</td>
			<td></td>
			<td></td>
			<td>https://git-scm.com/</td>
		</tr>
		<tr>
			<td>Inherent Dynamics Pipeline</td>
			<td></td>
			<td></td>
			<td>https://gitlab.com/biochron/inherent_dynamics_pipeline</td>
		</tr>
		<tr>
			<td>Inherent Dynamics Visualizer</td>
			<td></td>
			<td></td>
			<td>https://gitlab.com/bertfordley/inherent_dynamics_visualizer</td>
		</tr>
		<tr>
			<td>Miniconda</td>
			<td></td>
			<td></td>
			<td>https://docs.conda.io/en/latest/miniconda.html</td>
		</tr>
		<tr>
			<td>Pip</td>
			<td></td>
			<td></td>
			<td>https://pip.pypa.io/en/stable/</td>
		</tr>
	</tbody>
</table>

inherent dynamics visualizer, an interactive application for evaluating and visualizing outputs from a gene regulatory network inference pipeline

Developing gene regulatory network models is a major challenge in systems biology. Several computational tools and pipelines have been developed to tackle this challenge, including the newly developed Inherent Dynamics Pipeline. The Inherent Dynamics Pipeline consists of several previously published tools that work synergistically and are connected in a linear fashion, where the output of one tool is then used as input for the following tool. As with most computational techniques, each step of the Inherent Dynamics Pipeline requires the user to make choices about parameters that don't have a precise biological definition. These choices can substantially impact gene regulatory network models produced by the analysis. For this reason, the ability to visualize and explore the consequences of various parameter choices at each step can help increase confidence in the choices and the results.The Inherent Dynamics Visualizer is a comprehensive visualization package that streamlines the process of evaluating parameter choices through an interactive interface within a web browser. The user can separately examine the output of each step of the pipeline, make intuitive changes based on visual information, and benefit from the automatic production of necessary input files for the Inherent Dynamics Pipeline. The Inherent Dynamics Visualizer provides an unparalleled level of access to a highly intricate tool for the discovery of gene regulatory networks from time series transcriptomic data.

The steps described textually above and graphically in Figure 1 were applied to the core oscillating GRN of the yeast cell-cycle to see if it is possible to discover functional GRN models that are capable of producing the dynamics observed in time series gene expression data collected in a yeast cell-cycle study16. To illustrate how the IDV can clarify and improve IDP output, the results, after performing this analysis in two ways, were compared: 1) running all steps ...

Watch this Scientific Journal Video about Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline at JoVE.com

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

The Inherent Dynamics Visualizer is an interactive visualization package that connects to a gene regulatory network inference tool for enhanced, streamlined generation of functional network models. The visualizer can be used to make more informed decisions for parameterizing the inference tool, thus increasing confidence in the resulting models.

Developing gene regulatory network models is a major challenge in systems biology. Several computational tools and pipelines have been developed to tackle ...

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