ResponseNet Webserver FAQ
The ResponseNet WebServer 2.0 FAQ
Identifying signaling & regulatory pathways connecting your proteins & genes now with Human data!
For additional information:
Contact us at: Esti Yeger-Lotem at firstname.lastname@example.org
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 and genes, such as a set of disease-associated proteins 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 that are potentially involved in the studied condition and their likely modes of action. Computationally, ResponseNet is formulated as a minimum-cost flow optimization problem that is solved using linear programming.
The ResponseNet web server enables researchers that were previously limited to separate analysis of their distinct, large-scale experiments, to meaningfully integrate their data and substantially expand their understanding of the underlying cellular response.
We previously showed that in yeast genetic screens and transcriptomic profiling assays reveal distinct sets of genes, where genetic screens tend to identify response regulators and transcriptomic assays tend to identify metabolic processes (Yeger-Lotem et al., Nature Genetics 2009). ResponseNet was designed to bridge the gap between these data: Users can upload weighted lists of proteins and genes and obtain a sparse, weighted, molecular interaction sub-network connecting their data. This sub-network reveals additional proteins and interactions that are involved in the studied condition and uncovers their potential modes-of-action.
ResponseNet previously supported analysis of the yeast Saccharomyces cerevisiae. ResponseNet2.0 also supports the analysis of human (Homo sapiens) data.
To this end we integrated data of experimentally detected interactions among proteins and genes from several databases (BioGRID DIP, IntAct, MINT and TRANSFAC).
ResponseNet2.0 also provides enhanced functionality by giving more information on predicted proteins (gene ontology annotations, interacting small-molecules) and interactions (detection methods), by enabling node addition and removal from the output network, and by supporting randomization runs to enable the user to estimate the probability of observing a predicted protein by chance.
We support analysis of the yeast Saccharomyces cerevisiae and of human (Homo sapiens). You can run ResponseNet2.0 on other organisms given that you provide their interactome (enabled by clicking the more options menu).
Interactions data include protein-protein interactions, transcription regulation interactions between transcription factors and their target genes, and, coming soon, miR interactions between micro-RNAs and their target genes. We update our interactions data every 6 months.
Experimentally detected protein-protein interactions in yeast (S. cerevisiae) and human were obtained from four major databases (BioGRID, DIP, IntAct, and MINT). The yeast interactome currently contains 186,970 interactions between 6,010 proteins. The human interactome currently contain 76,320 interactions between 11,816 proteins.
For each interaction we provide the type of interaction detection method (e.g., Affinity Capture-Western), and database from which the PPI was extracted following a '@' sign (e.g., @). Interactions are weighted as described in Yeger-Lotem et al.
Transcription regulation interactions include experimental data only (yeast S. cerevisiae) or experimental data and predicted interactions (human). Yeast data include 14,010 interactions between 217 transcription factors and 4,357 genes, as described in Yeger-Lotem et al.
Human data sources include experimentally identified transcription regulation interactions downloaded from TRANSFAC (5,202 interactions between 453 transcription factors and 1,401 target genes) and were associated with a weight of 1.
Coming soon: Predicted interactions were downloaded from the UCSC human genome browser and limited to interactions detected in 100KB promoter regions of human genes conserved in mouse and rat (27,543 interactions between 119 transcription factors and 9746 target). The weights associated with these interactions reflect the prediction score and normalized to 0-0.5.
Coming soon: Experimentally identified miR to DNA interactions were downloaded from TarBase and miRecords. They include 17,695 interactions between 295 micro-RNAs and 9,350 target genes. Experimentally identified transcription regulation interactions between transcription factors and genes encoding micro-RNAs were downloaded from TransMIR. They include 403 interactions between 125 transcription factors and 144 micro-RNAs. An interactome containing these interactions will become available soon.
Most of the available protein-protein interactions data are oblivious to alternatively-spliced isoforms. Therefore, ResponseNet associates each protein-coding gene with a single protein product. We do not currently support alternative-spliced proteins for lack of protein-protein interactions data.
You can select proteins, genes or interactions by left-clicking over a protein or interaction. You can also select all nodes and interactions in an area by left-clicking and dragging your mouse. To activate the context menu you right-click your mouse in the network frame.
ResponseNet is formulated a sa minimum-cost flow optimization problem that is solved using linear programming. Gamma and capping are two parameters that appear in the objective function.
Gamma controls the connectivity between the source and target sets by multiplying the amount of flux in the network. Increasing gamma will incorporate lower-probability paths into the output network, while decreasing gamma will limit the network to the most likely paths. Thus gamma controls the size of your output network: 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.
Capping is an upper threshold on the probability of an interaction, and thus ranges between 0 and 1. In the objective function 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. To avoid long paths we set capping to a default value of 0.8.
For more details see Yeger-Lotem et al.
Please email Esti Yeger-Lotem at email@example.com
We would love to hear from you!
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