Name: inherent dynamics visualizer, an interactive application for evaluating and visualizing outputs from a gene regulatory network inference pipeline
Uploaded: 2021-12-07T13:30:00.000+00:00
Duration: 10 min 44 s
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. 
	NOTE: Cloning of the IDV should happen outside of the IDP&#39;s top-level directory.</li>
	<li>Follow the install instructions in the IDV&#39;s README file.</li>
</ol>
2. Node finding
<ol>
	<li>Create a new IDP configuration file that parametrizes the Node Finding step. 
	NOTE: All quotation marks in the following steps should not be typed out. The quotation marks are only used here as a delimiter between the protocol text and what is to be typed out.
	<ol>
		<li>Add the main IDP arguments to the configuration file.</li>
		<li>Open a new text file in a text editor and type &#34;data_file =&#34;, &#34;annotation_file =&#34;, &#34;output_dir =&#34;, &#34;num_proc =&#34;, and &#34;IDVconnection = True&#34; on individual lines.</li>
		<li>For &#34;data_file&#34;, after the equal to sign, type the path to and name of the respective time series file and type a comma after the name. Separate each data by a comma, if more than one time series data set is being used. See Supplemental File 1 and Supplemental File 2 for an example of time series gene expression files.</li>
		<li>Type the path to and name of the annotation file for &#34;annotation_file&#34;, after the equal to sign. See Supplemental File 3 for an example of an annotation file.</li>
		<li>For &#34;output_file&#34;, after the equal to sign, type the path to and name of the folder where results will be saved.</li>
		<li>After the equal to sign, for &#34;num_proc&#34;, type the number of processes the IDP should use.</li>
		<li>Add Node Finding arguments to the configuration file.</li>
		<li>In the same text file as in step 2.1.1, type in the order presented &#34;[dlxjtk_arguments]&#34;, &#34;periods =&#34;, and &#34;dlxjtk_cutoff =&#34; on individual lines. Place these after the main arguments.</li>
		<li>For &#34;periods&#34;, after the equal to sign, if one-time series data set is being used, type each period length separated by commas. For more than one time series data set, type each set of period lengths as before but place square brackets around each set and place a comma between the sets.</li>
		<li>After the equal to sign, for &#34;dlxjtk_cutoff&#34;, type an integer specifying the maximum number of genes to retain in the gene_list_file output by de Lichtenberg by JTK_CYCLE (DLxJTK) (Table 1). 
		NOTE: It is highly recommended to review the dlxjtk_arguments sections in the IDP README to get a better understanding of each argument. See Supplemental File 4 for an example of a configuration file with the Node Finding arguments specified.</li>
	</ol>
	</li>
	<li>In the terminal, move into the IDP directory, named inherent_dynamics_pipeline.</li>
	<li>In the terminal, enter the command: conda activate dat2net</li>
	<li>Run the IDP using the configuration file created in step 2.1 by running this command in the terminal, where &#60;config file name&#62; is the name of the file: python src/dat2net.py &#60;config file name&#62;</li>
	<li>In the terminal, move to the directory named inherent_dynamics_visualizer&#160;and enter the command: ./viz_results.sh &#60;results_directory&#62; 
	NOTE: &#60;results_directory&#62; will point to the directory used as the output directory for the IDP.</li>
	<li>In a web browser, enter&#160;http://localhost:8050/ as the URL.</li>
	<li>With the IDV now open in the browser, click on the Node Finding tab and select the node finding folder of interest from the dropdown menu.</li>
	<li>Manually curate a new gene list from the gene list table in the IDV to be used for subsequent IDP steps.
	<ol>
		<li>To extend or shorten the gene list table, click on the up or down arrows or manually enter in an integer between 1 and 50 in the box next to Gene expression of DLxJTK-ranked genes. Top:.</li>
		<li>In the gene list table, click on the box beside a gene to view its gene expression profile in a line graph. Multiple genes can be added.</li>
		<li>Optionally specify the number of equally sized bins to compute and order genes by the time interval containing their peak expression, by inputting an integer into the input box above the gene list table labeled Input integer to divide the first cycle into bins:. 
		NOTE: This option is specific to oscillatory dynamics and might not be applicable to other types of dynamics.</li>
		<li>Select a heatmap viewing preference by clicking on an option under Order Genes By: First Cycle Max Expression (Table 1) which orders genes based on the time of the gene-expression peak in the first cycle. 
		NOTE: DLxJTK Rank orders genes based on the periodicity ranking from the DLxJTK algorithm of the IDP.</li>
		<li>Click on the Download Gene List button to download the gene list into the file format needed for the Edge Finding step. See Supplemental File 5 for an example of a gene list file.</li>
	</ol>
	</li>
	<li>In the Editable Gene Annotation Table, label a gene as a target, a regulator, or both in the annotation file for the Edge Finding step in a new Edge Finding run. If a gene is a regulator, label the gene as an activator, repressor, or both.
	<ol>
		<li>To label a gene as an activator, click on the cell in the tf_act column and change the value to 1. To label a gene as a repressor, change the value in the tf_rep column to 1. A gene will be allowed to act as both an activator and a repressor in the Edge Finding step by setting the values in both the tf_act and tf_rep columns to 1.</li>
		<li>To label a gene as a target, click on the cell in the target column and change the value to 1.</li>
	</ol>
	</li>
	<li>Click on the Download Annot. File button to download the annotation file into the file format needed for the Edge Finding step.</li>
</ol>
3. Edge finding
<ol>
	<li>Create a new IDP configuration file that parametrizes the Edge Finding step.
	<ol>
		<li>Add the main IDP arguments to the configuration file. Open a new text file in a text editor and repeat step 2.1.1.</li>
		<li>Add Edge Finding arguments to the configuration file.</li>
		<li>In the same text file as in step 3.1.1, type in the order presented &#34;[lempy_arguments]&#34;, &#34;gene_list_file =&#34;, &#34;[netgen_arguments]&#34;, &#34;edge_score_column =&#34;, &#34;edge_score_thresho =&#34;, &#34;num_edges_for_list =&#34;, &#34;seed_threshold =&#34;, and &#34;num_edges_for_seed =&#34; on individual lines. These should go below the main arguments.</li>
		<li>For &#34;gene_list_file&#34;, after the equal to sign, enter the path to and name of the gene list file generated in step 2.8.5.</li>
		<li>For &#34;edge_score_column&#34;, after the equal to sign, enter either &#34;pld&#34; or &#34;norm_loss&#34; to specify which data frame column from the lempy output is used to filter the edges.</li>
		<li>Select either &#34;edge_score_threshold&#34; or &#34;num_edges_for_list&#34;, and delete the other. If &#34;edge_score_threshold&#34; was selected, enter a number between 0 and 1. This number will be used to filter edges based on the column specified in step 3.1.5.
		<ol>
			<li>If &#34;num_edges_for_list&#34; was selected, enter a value equal to or less than the number of possible edges. This number will be used to filter the edges based on how they are ranked in the column specified in step 3.1.5. The edges left over will be used to build networks in Network Finding.</li>
		</ol>
		</li>
		<li>Select either &#34;seed_threshold&#34; or &#34;num_edges_for_seed&#34; and delete the other. If &#34;seed_threshold&#34; was selected, enter a number between 0 and 1. This number will be used to filter edges based on the column specified in step 3.1.5.
		<ol>
			<li>If &#34;num_edges_for_seed&#34; was selected, enter a value equal to or less than the number of possible edges. This number will be used to filter the edges based on how they are ranked in the column specified in step 3.1.5. The edges left over will be used to build the seed network (Table 1) used in Network Finding. 
			NOTE: It is highly recommended to review the lempy_arguments and netgen_arguments sections in the IDP README to get a better understanding of each argument. See Supplemental File 7 for an example of a configuration file with the Edge finding arguments specified.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Repeat steps 2.2 and 2.3.</li>
	<li>Run the IDP using the configuration file created in step 3.1 by running this command in the terminal, where &#60;config file name&#62; is the name of the file: python src/dat2net.py &#60;config file name&#62;</li>
	<li>If the IDV is still running, stop it by pressing Control C in the terminal window to stop the program. Repeat steps 2.5 and 2.6.</li>
	<li>With the IDV open in the browser, click on the Edge Finding tab and select the edge finding folder of interest from the drop-down menu. 
	NOTE: If multiple datasets are used in Edge Finding, then make sure to select the last dataset that was used in the Local Edge Machine (LEM) analysis (Table 1). It is important when selecting edges for the seed network or edge list based on LEM results to look at the last time series data listed in the configuration file as this output incorporates all preceding datafiles in its inference of regulatory relationships between nodes.</li>
	<li>To extend or shorten the edge table, manually enter an integer in the input box under Number of Edges:.</li>
	<li>Optionally filter edges on the LEM ODE parameters. Click and drag to move either the left side or the right side of each parameter&#39;s slider to remove edges from the edge table that have parameters outside of their new allowed parameter bounds.</li>
	<li>Optionally create a new seed network if a different seed network is wanted than the one proposed by the IDP. See Supplemental File 8 for an example of a seed network file.
	<ol>
		<li>Select either From Seed to select the seed network or From Selection from the dropdown menu under Network:.</li>
		<li>Deselect/select edges from the edge table by clicking the corresponding checkboxes adjacent to each edge to remove/add edges from the seed network.</li>
	</ol>
	</li>
	<li>Click on the Download DSGRN NetSpec button to download the seed network in the Dynamic Signatures Generated by Regulatory Networks (DSGRN) (Table 1) network specification format.</li>
	<li>Select additional nodes and edges to be used in the Network Finding step.
	<ol>
		<li>Select edges from the edge table by clicking the corresponding checkboxes to include in the edge list file used in Network Finding.</li>
		<li>Click on Download Node and Edge Lists to download the node list and edge list files in the format required for their use in Network Finding. See Supplemental File 9 and Supplemental File 10 for examples of edge and node list files, respectively. 
		&#8203;NOTE: The node list must contain all the nodes in the edge list file, so the IDV automatically creates the node list file based on the selected edges. Two options are available for viewing the edges in Edge Finding. The LEM Summary Table option presents the edges as a ranked list of the top 25 edges. Top-Line LEM Table presents the edges in a concatenated list of the top three ranked edges for each possible regulator. The number of edges viewed for each option can be adjusted by the user by changing the number in the Number of Edges input box.</li>
	</ol>
	</li>
</ol>
4. Network finding
<ol>
	<li>Create a new IDP configuration file that parametrizes the Network Finding step.
	<ol>
		<li>Add the main IDP arguments to the configuration file. Open a new text file in a text editor and repeat step 2.1.1.</li>
		<li>Add Network Finding arguments to the configuration file.</li>
		<li>In the same text file as in step 4.1.1, type in the order presented &#34;[netper_arguments]&#34;, &#34;edge_list_file =&#34;, &#34;node_list_file =&#34;, &#34;seed_net_file =&#34;, &#34;range_operations =&#34;, &#34;numneighbors =&#34;, &#34;maxparams =&#34;, &#34;[[probabilities]]&#34;, &#34;addNode =&#34;, &#34;addEdge =&#34;, &#34;removeNode =&#34;, and &#34;removeEdge =&#34; on individual lines, below the main arguments.</li>
		<li>For &#34;seed_net_file&#34;, &#34;edge_list_file&#34; and &#34;node_list_file&#34;, after the equal sign, enter the path to and name of the seed network file and the edge and node list files generated in steps 3.9 and 3.10.2.</li>
		<li>After the equal to sign, for &#34;range_operations&#34;, type two numbers separated by a comma. The first and second numbers are the minimum and the maximum number of addition or removal of nodes or edges per network made, respectively.</li>
		<li>For &#34;numneighbors&#34;, after the equal to sign, enter a number that represents how many networks to find in Network Finding.</li>
		<li>For &#34;maxparams&#34;, after the equal to sign, enter a number that represents the maximum number of DSGRN parameters to allow for a network.</li>
		<li>Enter values between 0 and 1 for each of these arguments: &#34;addNode&#34;, &#34;addEdge&#34;, &#34;removeNode&#34;, and &#34;removeEdge&#34;, after the equal to sign. The numbers must sum to 1. 
		NOTE: It is highly recommended to review the netper_arguments and netquery_arguments sections in the IDP README to get a better understanding of each argument. See Supplemental File 11 and Supplemental File 12 for examples of a configuration file with the Network Finding arguments specified.</li>
	</ol>
	</li>
	<li>Repeat steps 2.2 and 2.3.</li>
	<li>Run the IDP using the configuration file created in step 4.1 by running this command in the terminal, where &#60;config file name&#62; is the name of the file: python src/dat2net.py &#60;config file name&#62;</li>
	<li>If the IDV is still running, stop it by pressing Control C in the terminal window to stop the program. Repeat steps 2.5 and 2.6.</li>
	<li>With the IDV open in the browser, click on the Network Finding tab and select the network finding folder of interest.</li>
	<li>Select a network or set of networks to generate an edge prevalence table (Table 1) and to view the networks along with their respective query results.
	<ol>
		<li>Two options are available for selecting networks: Option 1 - Input lower and upper bounds on query results by inputting minimum and maximum values in the input boxes corresponding to the x-axis and y-axis of the plot. Option 2 - Click and drag over the scatterplot to draw a box around the networks to be included. After selection or input bounds are entered, press the Get Edge Prevalence From Selected Networks button. 
		NOTE: If more than one DSGRN query was specified, use the radio buttons labeled with the query type to switch between each query&#39;s results. The same applies if more than one epsilon (noise level) was specified.</li>
	</ol>
	</li>
	<li>Click the arrows beneath the edge prevalence table to move to the next page of the table. Press Download Table to download the edge prevalence table.</li>
	<li>Input an integer in the Network Index input box to display a single network from the selection made in step 4.6. Click on Download DSGRN NetSpec to download the displayed network in the DSGRN network specification format.</li>
	<li>Search networks for similarity to a specified motif or network of interest.
	<ol>
		<li>Use the checkboxes corresponding to each edge to select edges to be included in the network or motif used for the similarity analysis. Click on Submit to create the similarity scatterplot for the selected motif or network. 
		NOTE: Use the arrows in the edge list to sort alphabetically and the arrows beneath the table to move to the next page of the table.</li>
		<li>Click and drag over the scatterplot to draw a box around the networks to be included to select a network or set of networks to generate an edge prevalence table and to view the networks along with their respective query results. 
		NOTE: If more than one DSGRN query was specified, use the radio buttons labeled with the query type to switch between each query&#39;s results. The same applies if more than one epsilon (noise level) was specified.</li>
		<li>Repeat steps 4.7 and 4.8 to download the edge prevalence table and the displayed network for the similarity analysis, respectively.</li>
	</ol>
	</li>
</ol>

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The authors have nothing to disclose.

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 individual steps of the GRN inference tools within the IDP. The IDV streamlines and facilitates the process of evaluating results from these computational inference methods to speed up experimentation and inform analysis choices, which in turn will allow for the accelerated production of high-confidence network models and hypotheses. The IDV also implements features that expand on the functionality of the IDP, including filtering edges by LEM ODE parameter choices, binning of genes by their expression time, and clustering networks based on similarity to a motif or network. Importantly, the IDV allows for manual interventions between each IDP step, which allows the user to easily incorporate human knowledge and prior information from the literature in ways that cannot be easily automated. A naive run of the IDP will not natively incorporate this information, so the use of the IDV will increase the confidence in the results whenever information specific to the experiment is available. Overall, using the IDV in conjunction with the IDP allows users to create network hypotheses for biological processes with greater confidence, even with little or no knowledge of the true GRN.
There are three critical steps in the IDV. The first is evaluating IDP Node Finding results in the IDV. IDV&#39;s Node Finding page can produce a new gene list and, if desired, a gene annotation file. Curating a new gene list is a critical step as it greatly reduces the potential network space by limiting which genes are allowed to be modeled as GRN targets and/or regulators. Additionally, as GRNs are mostly made up of transcription factors, having gene annotations will greatly help in creating coherent GRN models.
The next step is evaluating IDP Edge Finding results in the IDV. Curating a new seed network is a critical step since it localizes the region of network space that will be sampled in the Network Finding step. However, knowing where to start isn&#39;t always obvious, so it is recommended to use edges that have some form of experimental evidence to provide confidence that one is starting in a region of network space that contains high confidence edges. The IDV&#39;s Edge Finding page enables easy assembly of seed networks and generates the associated DSGRN network specification file as well as node and edge lists.
The last step is evaluating IDP Network Finding results in the IDV. IDV&#39;s Network Finding page allows for easy exploration of sampled networks and their associated scores that estimate the capacity of the network to produce the observed dynamics. While Node and Edge Finding will always return results (if at least two genes are passed on from Node Finding), Network Finding can return zero results. Therefore, knowing whether adjustments in parameters are needed will be more obvious in Network Finding than in Node and Edge Finding. Such occurrences of few to no networks found could be a result of constraints placed on what networks can be analyzed. These constraints are: 1) whether or not the networks are always strongly connected, 2) the minimum and the maximum number of input edges to each node, 3) the probabilities of adding and removing nodes and edges, and 4) the number of additions and removals of nodes and edges allowed. If few or no model-admissible networks are found, as in Figure 2, then referring to the IDP documentation is recommended for guidance on reparameterization of any or all steps of the IDP with subsequent evaluation of results in the IDV.
A current limitation of this approach is that the Node Finding page is mostly focused on oscillatory dynamics, such as those seen in the transcriptional programs of the cell-cycle and circadian clock. In particular, the IDP Node Finding step is currently configured to search for genes that exhibit oscillatory dynamics at a specified period. As the IDP expands to include analyses that can quantify different types of transcriptional dynamics, so too will the IDV be updated to support visualization and interrogation of these other behaviors. The size of networks searched for and analyzed in the Network Finding step are currently limited to networks of smaller size, e.g., around 10 genes. This is a necessity as computations in the DSGRN scale occur combinatorially. Another limitation is that exploring model parameter space for a selected network is not possible in the IDV. However, the DSGRN network specification file for a given network can be downloaded and the dynamics associated with each model parameter can be visualized on the DSGRN Visualization website (https://sites.math.rutgers.edu/~gameiro/dsgrn_viz/). Lastly, the IDV has been tested using Linux (Ubuntu) and iOS (Big Sur) systems. The IDV has been tested on Windows 10 using the Windows Subsystem for Linux (WSL), which allows Windows 10 users to run Linux and the IDV without the need for a different computer, a virtual machine, or a dual-boot setup. IDV does not currently run on native Windows.
Studying GRNs is difficult due to their inherent complexity and useful inference tools such as the IDP can be difficult to understand and deploy with confidence. The IDV provides a method to reduce the complexity of studying GRNs inferred using the IDP while facilitating the inclusion of additional information beyond gene expression dynamics. Using the IDV in conjunction with the IDP as described here will empower researchers to develop and analyze functional models of well-studied systems, such as the human cell-cycle. Furthermore, these tools will generate testable hypotheses for less understood processes, such as the malaria intra-erythrocytic development cycle, which is suspected to be controlled by a GRN24 but for which a model is yet to be proposed.

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><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.

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 ...

inherent-dynamics-visualizer-an-interactive-application-for

Nanyang Technological University

Research

JoVE Journal

Biology

2.1K Views.  Duke University. De Inherent Dynamics Visualizer maakt het werken met verschillende inferentiemethoden en het begrijpen van hun resultaten gemakkelijker voor onderzoekers, wat op zijn beurt de versnelde productie van functionele netwerkmodellen mogelijk maakt. Het belangrijkste voordeel van de Inherent Dynamics Visualizer is het visualiseren en verkennen van tussenresultaten voor het informeren van parameters en de inhoud van invoerbestanden van downstream-stappen. De Inherent Dynamics Visualizer vergemakkeli...

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

Exploring the Effects of Spaceflight on Mouse Physiology using the Open Access NASA GeneLab Platform

Performing biological experiments in space requires special accommodations and procedures to ensure that these investigations are performed effectively and efficiently. Moreover, given the infrequency of these experiments it is imperative that their impacts be maximized. The rapid advancement of omics technologies offers an opportunity to dramatically increase the volume of data produced from precious spaceflight specimens. To capitalize on this, NASA has developed the GeneLab platform to provide unrestricted access to spaceflight omics data and encourage its widespread analysis. Rodents (both rats and mice) are common model organisms used by scientists to investigate space-related biological impacts. The enclosure that house rodents during spaceflight are called Rodent Habitats (formerly Animal Enclosure Modules), and are substantially different from standard vivarium cages in their dimensions, air flow, and access to water and food. In addition, due to environmental and atmospheric conditions on the International Space Station (ISS), animals are exposed to a higher CO2 concentration. We recently reported that mice in the Rodent Habitats experience large changes in their transcriptome irrespective of whether animals were on the ground or in space. Furthermore, these changes were consistent with a hypoxic response, potentially driven by higher CO2 concentrations. Here we describe how a typical rodent experiment is performed in space, how omics data from these experiments can be accessed through the GeneLab platform, and how to identify key factors in this data. Using this process, any individual can make critical discoveries that could change the design of future space missions and activities.

The NASA GeneLab platform provides unfettered access to precious omics data from biological spaceflight experiments. We describe how a typical mouse experiment is conducted in space and how data from such experiments can be accessed and analyzed.

Performing biological experiments in space requires special accommodations and procedures to ensure that these investigations are performed effectively ...

Genetics

A Practical Approach to Genetic Inducible Fate Mapping: A Visual Guide to Mark and Track Cells In Vivo

Fate maps are generated by marking and tracking cells in vivo to determine how progenitors contribute to specific structures and cell types in developing and adult tissue. An advance in this concept is Genetic Inducible Fate Mapping (GIFM), linking gene expression, cell fate, and cell behaviors in vivo, to create fate maps based on genetic lineage.
GIFM exploits X-CreER lines where X is a gene or set of gene regulatory elements that confers spatial expression of a modified bacteriophage protein, Cre recombinase (CreERT). CreERT contains a modified estrogen receptor ligand binding domain which renders CreERT sequestered in the cytoplasm in the absence of the drug tamoxifen. The binding of tamoxifen releases CreERT, which translocates to the nucleus and mediates recombination between DNA sequences flanked by loxP sites. In GIFM, recombination typically occurs between a loxP flanked Stop cassette preceding a reporter gene such as GFP.
Mice are bred to contain either a region- or cell type-specific CreER and a conditional reporter allele. Untreated mice will not have marking because the Stop cassette in the reporter prevents further transcription of the reporter gene. We administer tamoxifen by oral gavage to timed-pregnant females, which provides temporal control of CreERT release and subsequent translocation to the nucleus removing the Stop cassette from the reporter. Following recombination, the reporter allele is constitutively and heritably expressed. This series of events marks cells such that their genetic history is indelibly recorded. The recombined reporter thus serves as a high fidelity genetic lineage tracer that, once on, is uncoupled from the gene expression initially used to drive CreERT.
We apply GIFM in mouse to study normal development and ascertain the contribution of genetic lineages to adult cell types and tissues. We also use GIFM to follow cells on mutant genetic backgrounds to better understand complex phenotypes that mimic salient features of human genetic disorders.
This video article guides researchers through experimental methods to successfully apply GIFM. We demonstrate the method using our well characterized Wnt1-CreERT;mGFP mice by administering tamoxifen at embryonic day (E)8.5 via oral gavage followed by dissection at E12.5 and analysis by epifluorescence stereomicroscopy. We also demonstrate how to micro-dissect fate mapped domains for explant preparation or FACS analysis and dissect adult fate-mapped brains for whole mount fluorescent imaging. Collectively, these procedures allow researchers to address critical questions in developmental biology and disease models.

A Practical Approach to Genetic Inducible Fate Mapping: A Visual Guide to Mark and Track Cells In Vivo

Genetic Inducible Fate Mapping (GIFM) marks and tracks cells with fine spatial and temporal control in vivo and elucidates how cells from a specific genetic lineage contribute to developing and adult tissues. Demonstrated here are the techniques required to fate map E12.5 mouse embryos for epifluorescent and explant analysis.

Fate maps are generated by marking and tracking cells in vivo to determine how progenitors contribute to specific structures and cell types in developing ...

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

With recent advances in mass spectrometry-based proteomics technologies, deep profiling of hundreds of proteomes has become increasingly feasible. However, deriving biological insights from such valuable datasets is challenging. Here we introduce a systems biology-based software JUMPn, and its associated protocol to organize the proteome into protein co-expression clusters across samples and protein-protein interaction (PPI) networks connected by modules (e.g., protein complexes). Using the R/Shiny platform, the JUMPn software streamlines the analysis of co-expression clustering, pathway enrichment, and PPI module detection, with integrated data visualization and a user-friendly interface. The main steps of the protocol include installation of the JUMPn software, the definition of differentially expressed proteins or the (dys)regulated proteome, determination of meaningful co-expression clusters and PPI modules, and result visualization. While the protocol is demonstrated using an isobaric labeling-based proteome profile, JUMPn is generally applicable to a wide range of quantitative datasets (e.g., label-free proteomics). The JUMPn software and protocol thus provide a powerful tool to facilitate biological interpretation in quantitative proteomics.

We present a systems biology tool JUMPn to perform and visualize network analysis for quantitative proteomics data, with a detailed protocol including data pre-processing, co-expression clustering, pathway enrichment, and protein-protein interaction network analysis.

With recent advances in mass spectrometry-based proteomics technologies, deep profiling of hundreds of proteomes has become increasingly feasible. ...

Biochemistry

Real-time Bioluminescence Imaging of Notch Signaling Dynamics during Murine Neurogenesis

Notch signaling regulates the maintenance of neural stem/progenitor cells by cell-cell interactions. The components of Notch signaling exhibit dynamic expression. Notch signaling effector Hes1 and the Notch ligand Delta-like1 (Dll1) are expressed in an oscillatory manner in neural stem/progenitor cells. Because the period of the oscillatory expression of these genes is very short (2 h), it is difficult to monitor their cyclic expression. To examine such rapid changes in the gene expression or protein dynamics, fast response reporters are required. Because of its fast maturation kinetics and high sensitivity, the bioluminescence reporter luciferase is suitable to monitor rapid gene expression changes in living cells. We used a destabilized luciferase reporter for monitoring the promoter activity and a luciferase-fused reporter for visualization of protein dynamics at single cell resolution. These bioluminescence reporters show rapid turnover and generate very weak signals; therefore, we have developed a highly sensitive bioluminescence imaging system to detect such faint signals. These methods enable us to monitor various gene expression dynamics in living cells and tissues, which are important information to help understand the actual cellular states.

Neural stem/progenitor cells exhibit various expression dynamics of Notch signaling components that lead to different outcomes of cellular events. Such dynamic expression can be revealed by real-time monitoring, not by static analysis, using a highly sensitive bioluminescence imaging system that enables visualization of rapid changes in gene expressions.

Notch signaling regulates the maintenance of neural stem/progenitor cells by cell-cell interactions. The components of Notch signaling exhibit dynamic ...

Developmental Biology

Neurovascular Network Explorer 2.0: A Simple Tool for Exploring and Sharing a Database of Optogenetically-evoked Vasomotion in Mouse Cortex In Vivo

The importance of sharing experimental data in neuroscience grows with the amount and complexity of data acquired and various techniques used to obtain and process these data. However, the majority of experimental data, especially from individual studies of regular-sized laboratories never reach wider research community. A graphical user interface (GUI) engine called Neurovascular Network Explorer 2.0 (NNE 2.0) has been created as a tool for simple and low-cost sharing and exploring of vascular imaging data. NNE 2.0 interacts with a database containing optogenetically-evoked dilation/constriction time-courses of individual vessels measured in mice somatosensory cortex in vivo by 2-photon microscopy. NNE 2.0 enables selection and display of the time-courses based on different criteria (subject, branching order, cortical depth, vessel diameter, arteriolar tree) as well as simple mathematical manipulation (e.g. averaging, peak-normalization) and data export. It supports visualization of the vascular network in 3D and enables localization of the individual functional vessel diameter measurements within vascular trees.
NNE 2.0, its source code, and the corresponding database are freely downloadable from UCSD Neurovascular Imaging Laboratory website1. The source code can be utilized by the users to explore the associated database or as a template for databasing and sharing their own experimental results provided the appropriate format.

A graphical user interface for exploring and sharing a database of optogenetically-induced vascular responses in mouse somatosensory cortex in vivo measured by 2-photon microscopy is presented. It allows browsing the data, criteria-based selection, averaging, localization of measurements within a 3D volume of vasculature and exporting the data.

The importance of sharing experimental data in neuroscience grows with the amount and complexity of data acquired and various techniques used to obtain ...

Neuroscience

Time-lapse Live Imaging and Quantification of Fast Dendritic Branch Dynamics in Developing Drosophila Neurons

Highly motile dendritic filopodia are widely present in neurons at early developmental stages. These exploratory dynamic branches sample the surrounding environment and initiate contacts with potential synaptic partners. Although the connection between dendritic branch dynamics and synaptogenesis is well established, how developmental and activity-dependent processes regulate dendritic branch dynamics is not well understood. This is partly due to the technical difficulties associated with the live imaging and quantitative analyses of these fine structures using an in vivo system. We established a method to study dendrite dynamics using Drosophila larval ventral lateral neurons (LNvs), which can be individually labeled using genetic approaches and are accessible for live imaging. Taking advantage of this system, we developed protocols to capture branch dynamics of the whole dendritic arbor of a single labeled LNv through time-lapse live imaging. We then performed post-processing to improve image quality through drift correction and deconvolution, followed by analyzing branch dynamics at the single-branch level by annotating spatial positions of all branch terminals. Lastly, we developed R scripts (Supplementary File) and specific parameters to quantify branch dynamics using the coordinate information generated by the terminal tracing. Collectively, this protocol allows us to achieve a detailed quantitative description of branch dynamics of the neuronal dendritic arbor with high temporal and spatial resolution. The methods we developed are generally applicable to sparsely labeled neurons in both in vitro and in vivo conditions.

Time-lapse Live Imaging and Quantification of Fast Dendritic Branch Dynamics in Developing Drosophila Neurons

Here, we describe the method we employed to image highly motile dendritic filopodia in a live preparation of the Drosophila larval brain, and the protocol we developed to quantify time-lapse 3D imaging datasets for quantitative assessments of dendrite dynamics in developing neurons.

Highly motile dendritic filopodia are widely present in neurons at early developmental stages. These exploratory dynamic branches sample the surrounding ...

A Web Tool for Generating High Quality Machine-readable Biological Pathways

PathWhiz is a web server built to facilitate the creation of colorful, interactive, visually pleasing pathway diagrams that are rich in biological information. The pathways generated by this online application are machine-readable and fully compatible with essentially all web-browsers and computer operating systems. It uses a specially developed, web-enabled pathway drawing interface that permits the selection and placement of different combinations of pre-drawn biological or biochemical entities to depict reactions, interactions, transport processes and binding events. This palette of entities consists of chemical compounds, proteins, nucleic acids, cellular membranes, subcellular structures, tissues, and organs. All of the visual elements in it can be interactively adjusted and customized. Furthermore, because this tool is a web server, all pathways and pathway elements are publicly accessible. This kind of pathway &#34;crowd sourcing&#34; means that PathWhiz already contains a large and rapidly growing collection of previously drawn pathways and pathway elements. Here we describe a protocol for the quick and easy creation of new pathways and the alteration of existing pathways. To further facilitate pathway editing and creation, the tool contains replication and propagation functions. The replication function allows existing pathways to be used as templates to create or edit new pathways. The propagation function allows one to take an existing pathway and automatically propagate it across different species. Pathways created with this tool can be &#34;re-styled&#34; into different formats (KEGG-like or text-book like), colored with different backgrounds, exported to BioPAX, SBGN-ML, SBML, or PWML data exchange formats, and downloaded as PNG or SVG images. The pathways can easily be incorporated into online databases, integrated into presentations, posters or publications, or used exclusively for online visualization and exploration. This protocol has been successfully applied to generate over 2,000 pathway diagrams, which are now found in many online databases including HMDB, DrugBank, SMPDB, and ECMDB.

PathWhiz is a comprehensive, online pathway drawing tool for generating biochemical and biological pathways. It uses publicly accessible databases and easily expandable palettes consisting of pre-drawn pathway components. This protocol describes how to easily build new pathways, replicate and edit existing pathways, and propagate previously drawn pathways to different organisms.

PathWhiz is a web server built to facilitate the creation of colorful, interactive, visually pleasing pathway diagrams that are rich in biological ...

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

The root microbiome plays an important role in plant growth and environmental adaptation. Network analysis is an important tool for studying communities, which can effectively explore the interaction relationship or co-occurrence model of different microbial species in different environments. The purpose of this manuscript is to provide details on how to use the weighted correlation network algorithm to analyze different co-occurrence networks that may occur in microbial communities due to different ecological environments. All analysis of the experiment is performed in the WGCNA package. WGCNA is an R package for weighted correlation network analysis. The experimental data used to demonstrate these methods were microbial community data from the NCBI (National Center for Biotechnology Information) database for three niches of the rice (Oryza sativa) root system. We used the weighted correlation network algorithm to construct co-abundance networks of microbial community in each of the three niches. Then, differential co-abundance networks among endosphere, rhizoplane and rhizosphere soil were identified. In addition, the core genera in network were obtained by the "WGCNA" package, which plays an important regulated role in network functions. These methods enable researchers to analyze the response of microbial network to environmental disturbance and verify different microbial ecological response theories. The results of these methods show that the significant differential microbial networks identified in the endosphere, rhizoplane and rhizosphere soil of rice.

Network analysis was applied to evaluate the association of various ecological microbial communities, such as soil, water and rhizosphere. Presented here is a protocol on how to use the WGCNA algorithm to analyze different co-occurrence networks that may occur in the microbial communities due to different ecological environments.

The root microbiome plays an important role in plant growth and environmental adaptation. Network analysis is an important tool for studying communities, ...

Addressing Metabolomics Data Analysis Using Advanced Bioinformatics Tools

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data

A significant challenge in the analysis of omics data is extracting actionable biological knowledge. Metabolomics is no exception. The general problem of relating changes in levels of individual metabolites to specific biological processes is compounded by the large number of unknown metabolites present in untargeted liquid chromatography-mass spectrometry (LC-MS) studies. Further, secondary metabolism and lipid metabolism are poorly represented in existing pathway databases. To overcome these limitations, our group has developed several tools for data-driven network construction and analysis. These include CorrelationCalculator and Filigree. Both tools allow users to build partial correlation-based networks from experimental metabolomics data when the number of metabolites exceeds the number of samples. CorrelationCalculator supports the construction of a single network, while Filigree allows building a differential network utilizing data from two groups of samples, followed by network clustering and enrichment analysis. We will describe the utility and application of both tools for the analysis of real-life metabolomics data.

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis 

We present CorrelationCalculator and Filigree, two tools for data-driven network construction and analysis of metabolomics data. CorrelationCalculator supports building a single interaction network of metabolites based on expression data, while Filigree allows building a differential network, followed by network clustering and enrichment analysis.

A significant challenge in the analysis of omics data is extracting actionable biological knowledge. Metabolomics is no exception. The general problem of ...

GAT Framework for Identifying Dark Biomarkers in Transcriptomes

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

Transcriptome represents the expression levels of many genes in a sample and has been widely used in biological research and clinical practice. Researchers usually focused on transcriptomic biomarkers with differential representations between a phenotype group and a control group of samples. This study presented a multitask graph-attention network (GAT) learning framework to learn the complex inter-genic interactions of the reference samples. A demonstrative reference model was pre-trained on the healthy samples (HealthModel), which could be directly used to generate the model-based quantitative transcriptional regulation (mqTrans) view of the independent test transcriptomes. The generated mqTrans view of transcriptomes was demonstrated by prediction tasks and dark biomarker detection. The coined term &#34;dark biomarker&#34; stemmed from its definition that a dark biomarker showed differential representation in the mqTrans view but no differential expression in its original expression level. A dark biomarker was always overlooked in traditional biomarker detection studies due to the absence of differential expression. The source code and the manual of the pipeline HealthModelPipe can be downloaded from&#160;http://www.healthinformaticslab.org/supp/resources.php.

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Here, we introduce a protocol for converting transcriptomic data into a mqTrans view, enabling the identification of dark biomarkers. While not differentially expressed in conventional transcriptomic analyses, these biomarkers exhibit differential expression in the mqTrans view. The approach serves as a complementary technique to traditional methods, unveiling previously overlooked biomarkers.

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

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