ResponseNet Webserver Tutorial
The ResponseNet WebServer 2.0 Tutorial
Identifying signaling & regulatory pathways connecting your proteins & genes – now with Human data!
To go back to the ResponseNet home page click here.
Please see our FAQ page.
Below you will find an Overview of ResponseNet 2.0, descriptions of its Welcome page, the Run ResponseNet page, ResponseNet output and the sample output.
Contact: For questions or feedback please email Esti Yeger-Lotem at estiyl@bgu.ac.il
ResponseNet is a computational framework that identifies high-probability signaling and regulatory paths that connect input data sets.
The input includes two weighted lists of condition-related proteins or genes, such as a set of disease-associated genes and a set of differentially expressed disease genes, and a molecular interaction network (i.e., interactome).
The output is a sparse, high-probability interactome sub-network connecting the two sets that is biased toward signaling pathways.
This sub-network exposes additional proteins and interactions that are potentially involved in the studied condition and their likely modes of action.
ResponseNet 2.0 supports the interactomes of the yeast Saccharomyces cerevisiae and of human (Homo sapiens - New!)
You may also apply ResponseNet to other organisms given that you provide a weighted interactome.
PLEASE NOTE THAT RUNNING RESPONSENET TAKES A FEW MINUTES due to the need to solve a linear programming problem with thousands of parameters.
Detailed information about the ResponseNet framework and its application are found in:
Yeger-Lotem E., Riva L., Su L.J., Gitler A., Cashikar A., King O.D., Auluck P.K., Geddie M.L., Valastyan J.S., Karger D.R., Lindquist S., Fraenkel E. 2009.
Bridging the gap between high-throughput genetic and transcriptional data reveals cellular pathways responding to alpha-synuclein toxicity. Nature Genetics 41: 316 - 323.
The welcome page is divided into two parts: Select what to do which offers ResponseNet analysis and session upload for returning users and New to ResponseNet 2.0? which offers additional information and a link to the tutorial, as described below.
This page also presents three icons that are constant across all pages:
links to ResponseNet2.0 homepage from every page.
opens ResponseNet2.0 homepage in a new tab.
links to ResponseNet2.0 tutorial from every page.
Select options:
- Run ResponseNet: Click if you want to run ResponseNet
- Load Session: Click if you are a returning user with a login that wants to upload previous jobs
- Change User: By default users are logged in as guests. If you would like your jobs to be accessed from other computers or at other times without using output links, use the change user option and log in as a user. User jobs are maintained for 30 days.
New to ResponseNet?
ResponseNet supports gene names in three formats: Common name, Ensembl Gene ID and Entrez Gene ID. The lists of genes are provided for each format.
The panel also contains a link to the tutorial, FAQ and previously published papers.
To cite the ResponseNet web-server please use:
Lan et al, ResponseNet: revealing signaling and regulatory networks linking genetic and transcriptomic screening data.
Nucleic Acids Res. 39 (suppl 2): W424-W429 (2011)
This page allows you to provide the following parameters:
Job details:
Job name: A few words that will appear as the header of the output and can be used to find the job at a later time
Job description (optional): A few words or sentences that will appear in the output, can be used to describe the input parameters.
ResponseNet currently provides the interactomes of the yeast Saccharomyces cerevisiae and human (Homo sapiens). To apply to a different organism replace the interactome data with data of known interactions in the organism of your choice.
Naming conventions:
- The interactomes we provide are dated (e.g., Yeast January 2011).
- Interactomes named 'with regulatory' also contain directed interactions between transcription factors if one regulates the transcription of the other.
- Interactomes named 'TF predictions <number>' include predicted interactions between transcription factors and target genes based on the UCSC analysis of transcription factor binding sites within conserved promoters. The number indicates the area upstream the promoter where the binding site was located.
Proteins and genes to be connected:
This is where you provide the input source and target sets that will be connected through molecular interactions. By default the source set is composed of proteins and the target set is composed of genes, but these can be changed.
The source and target set should be related to a common stimulus or condition.
ResponseNet proved to be particularly useful for identifying previously hidden signaling and regulatory relationships between proteins (source set) and differentially expressed genes (target set) detected upon exposing cells to a common genetic or biochemical stimulus.
Gene ID: ResponseNet currently supports yeast ORFS, Ensembl gene ids, Entrez Gene ids, and wiki gene names.
Source & Target Sets: Please provide a weighted list of gene ids. The format of each line is: <gene_id>\t<node type>(\t<weight>\t<capacity> optional)\n.
Node type is Protein or Gene.
Weight is any number between 0-1. By increasing the weight you increase the probability that the gene will appear in the output network. If no weights are given, weights are assumed to be uniform.
Capacity is any number between 0-1. By increasing the capacity you increase the probability that the gene will appear in the output network. If no capacity is given it is set to 1.
Source & target Lists: When checked, you may add to the source set by typing into the text box.
Interactions: This menu allows you to append interactions to the interactome or replace the entire interactome.
The interactome is currently composed of a list of experimentally-detected protein-protein interactions gathered from BioGRID, DIP, IntAct, and MINT and a list of experimentally-detected protein-DNA interactions gathered as described in Yeger-Lotem et al. for yeast and from TRANSFAC for human.
The format of each line is: <source node_id>\t<target node_id>\t<source node type>\t<target node type>\t<interaction type>\t<weight>(\t <direction>\t<capacity> optional)\n.
Weight is any number between 0-1. By increasing the weight you increase the probability that the gene will appear in the output network. If no weights are given, weights are assumed to be uniform.
Capacity is any number between 0-1. By increasing the capacity you increase the probability that the gene will appear in the output network. If no capacity is given it is set to 1. Interactions format: each line contains "gene1_id gene2_id probability"
By default interactions are considered bidirectional. You may add directed edges to the interactome by ending the interaction line with 1 (e.g., " YPL012W YLR293C Protein Protein PPI 0.763637131966 1"); the edge will be directed from the left-most protein (YPL012W) to the other (YLR293C).
Interaction List: When checked, you may add interactions to the interactome by typing into the text box. You can also separate your values using commas in the text box.
Gamma: This parameter controls the size of your output network. The default gamma value in yeast is 10 and in human is 2.5.
Typically, when gamma < 1 no solution is found and the output network is empty; when gamma > 20 the output network is saturated and cannot include additional paths.
Motivation: ResponseNet is formulated as a minimum-cost flow optimization problem and solving it efficiently using linear programming tools. The gamma parameter controls the connectivity between the source and target sets by multiplying the amount of flux in the network. Increasing gamma will thus incorporate lower-probability paths into the output network, while decreasing gamma will remove such paths from the network.
For more details see Yeger-Lotem et al.
Capping: This parameter controls the length of your output paths. The default capping value is 0.7, the minimum value is 0 (no path) and the maximum value is 1 (long paths).
Motivation: Capping is an upper threshold on the probability of an interaction. ResponseNet is formulated as a minimum-cost flow optimization problem and the cost of an edge representing an interaction is –log(interaction probability). Therefore, interactions with probability p=1 have a cost of 0, while low-probability interactions have high costs. If no capping is used and the network contains a non-negligible number of low cost edges, than the output network may contain long interaction paths. By using capping we put a lower threshold on edge costs.
For more details see Yeger-Lotem et al.
Default values for gamma and capping: We recommend using the default values for each organism. In general, for an interactome of over 120,000 interactions a gamma of 10 and above is recommended, while for an interactome of about 80,000 interactions a gamma that is less than 5 is recommended. The default values for human were selected upon evaluating ResponseNet by using SPIKE maps (Paz et al, 2011). We tested capping values ranging between 0.7-1 (with steps of 0.1) and gamma values ranging between 1-50 (for 1-5 with steps of 0.5; for 5-50 with steps of 5). Recommended values are those where the output network was close to saturation, meaning that increasing gamma did not add new nodes and edges to the output, and did not increase the sensitivity of the output. These values were quite similar across all SPIKE maps. The default values for yeast were selected as described in Yeger-Lotem et al, 2009.
Randomizations: Use randomization to estimate the probability of observing a certain protein in the output by chance. You can randomize the source set, target set or both; in each case a set of equal size will be chosen randomly from the interactome.
The number of times a protein was observed will appear in the output properties tab.
Run as shortest paths: Identifies all shortest paths connecting source and target sets, without optimization.
Run sample: A default run showing the connectivity between BRAF (source) and X (target) using default parameters.
By hitting Submit ResponseNet will identify the sub-network connecting your source and target sets.
PLEASE NOTE THAT RUNNING RESPONSENET TAKES A FEW MINUTES due to the need to solve a linear programming problem with thousands of parameters.
The output is divided into a network frame with a context menu and textual tabs
The frame presents the interaction network connecting a subset of your source set to a subset of your target set using Cytoscape-Web.
Network edges represent molecular interactions and are directed according to the flow.
If the target set was composed of genes then the last edge on each path represents a regulatory interaction between a transcription factor protein and its target gene.
For more details see Yeger-Lotem et al.
Network legend:
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Source-set protein |
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Protein predicted by ResponseNet |
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Transcription factor predicted by ResponseNet |
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Target-set gene |
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Micro-RNA |
By right clicking your mouse you can perform changes to graph layout, remove nodes, and import, export and save the session.
Get additional TFT: details the number of target genes that are also regulated by the same transcription factor, but are not presented due to the parsimonious nature of ResponseNet solution.
Tabs:
Provide additional information on ResponseNet run.
Properties: When you select nodes and edges in the network their information is presented in this tab.
Nodes information:
- Node name: Common Name, ENSG (Ensembl gene ID) and Entrez gene ID.
- Incoming flow: the flow value computed by ResponseNet, higher value means the node is more central.
- Randomization information: if randomization analysis was preformed, the number of times the node appeared as a source, target, or predicted as an intermediate.
Edges information:
- SourceNode and targetNode: the names of the connected proteins; the edge is directed from the source to the target.
- Random: if randomization analysis was preformed, the number of times the edge appeared in the randomized runs.
- Type: Can be PPI, i.e., protein-protein interactions, or transcription regulation, i.e., protein-DNAS interaction. Currently only PPIs are supported. The detection method is followed by a @ sign and by the name of the database reporting the interaction (e.g. Affinity Capture-MS@BIOGRID).
- Weight – the edge weight defined in the interactome.
- Flow – the flow value computed by ResponseNet, higher value means the edge is more central.
Layers: The user can upload another network through the context menu (Import→Graphml as Layer), or to create a new layer from the existing network through the context menu after selecting nodes and edges (New layer from selected). Once a layer was added, the user may unite layers, intersect them to identify common components, or hide common components through the show symmetric differences options. To enable these operations select layers and nodes, and click the required action.
Gene Ontology: When you select nodes in the network their gene ontology (GO) annotation will appear. Pressing the plus sign (+) next to a node presents the gene description from GO, and the three GO categories: Biological Process, Cellular Component and Molecular Function. The number of GO terms appears in (). Each GO term appears with its GO id, description and evidence code.
GO annotations were downloaded from the GO database.
Chemicals: When you select nodes from the network the chemicals that interact with the node will appear nex to the node name, the number of chemicals appears in ().
Protein-chemical interactions were downloaded from the STITCH3 database (Kuhn et al, Nucleic Acids Res. 2012).
Output Summary: Summarizes the parameters of the input sets.
Reachable = on a path connecting the source and target sets.
Regulated genes = are adjacent to an incoming transcription regulation interaction.
Downloads: The input and output network files.
ResponseNet Log: reports errors in the mapping of input sets.
Graph Options: presents options that are also accessible from the context menu.
Below we illustrate the use of ResponseNet2.0.
Source set proteins:
BAP1
Target set genes:
TWIST1
FOSL1
SPARC
TNC
UTRN
GDP2
MTIF2
TOMM70A
Results:
BAP1 is a gene that when mutated leads to melanoma, and the target genes were found to be differentially expressed in melanoma cell lines. ResponseNet2.0 predicted the sub-network connecting the source set protein BAP1 to the target genes. The sub-network contains the predicted intermediate protein HCFC1 that is connected to two predicted transcription factors SP1 and GABPA. The proteins are connected to each other by known protein-protein interactions, and the two transcription factors are connected to their target genes through regulatory interactions. S and T are auxiliary nodes that are part of ResponseNet formulation and can be ignored.
Notably, the two predicted transcription factors were previously linked to melanoma (see GABPA in http://www.biograph.be/concept/graph/C0004565/C1414905, and SP1 inhttp://www.nature.com/jid/journal/v113/n5/abs/5600506a.html)
Dealing with large source and target sets: Source and target sets may contain tens to thousands of proteins and genes. The ResponseNet output network obtained for such inputs is typically large and hard to view using the ResponseNet cytoscape-web interface. We recommend downloading the output network as an xgmml and uploading it into Cytoscape for further analysis. We also recommend removing S and T from the network and using Cytoscape hierarchical layout, which often divides the network into smaller and manageable modules. Click here for a view of a ResponseNet2.0 output human network obtained upon connecting a source set including 38 proteins to a target set including 56 genes. These sets were extracted from the SPIKE hearing and vision map (Paz et al 2011).
- Yeger-Lotem E., Riva L., Su L.J., Gitler A., Cashikar A., King O.D., Auluck P.K., Geddie M.L., Valastyan J.S., Karger D.R., Lindquist S., Fraenkel E. (2009). Bridging the gap between high-throughput genetic and transcriptional data reveals cellular pathways responding to alpha-synuclein toxicity. Nature Genetics 41: 316 - 323.
- Alex Lan, Ilan Y Smoly, Guy Rapaport, Susan Lindquist, Ernest Fraenkel & Esti Yeger-Lotem
ResponseNet: revealing signaling and regulatory networks linking genetic and transcriptomic screening data.
Nucleic Acids Res. 39 (suppl 2): W424-W429 (2011) - Lopes,C.T., Franz,M., Kazi,F. et al. (2010) Cytoscape Web: an interactive web-based network browser. Bioinformatics, 26, 2347–2348.
- Paz, A., Brownstein, Z., Ber, Y., Bialik, S., David, E., Sagir, D., Ulitsky, I., Elkon, R., Kimchi, A., Avraham, K.B. et al. (2011) SPIKE: a database of highly curated human signaling pathways. Nucleic Acids Res, 39, D793-799.