It is recommended to use one of the pre-formatted published protein-protein interaction networks:
Each mRNA is associated with a commodity and a weight representing the importance of that mRNA in the network. While these values usually represent differential expression, they can also be significance or even correlation with a phenotype of interest. For details see Gosline et al.
If using our provided TF-DNA interactions, genes must be in HUGO gene name format. A sample sink weights file is available here, where the weight is set as the correlation value between the mRNA expression and their associated microRNA (commodity) expression levels across 849 breast cancer patients from.
It is also possible to assign weights directly to proteins without adding TF-DNA interactions . If so, these weights should be annotated with UNIPROT Entry Names and you must check the "Use sink weights directly" checkbox. A template for the input format is available below:
The second input file requires a set of proteins of interest from each experiment ('commodity') be annotated by a weight signifying its importance in the experiment. For details see Gosline et al. Sample protein weights are provided here, where the weight is set as the TargetScan context+ score for the particular protein and its associated microRNA (commodity). SAMNet takes the absolute value of any negative numbers.
A template for the input format is available below:
Please note: The naming of the protein nodes in the protein weights input file must be consistent with the naming of the protein nodes in the interactome, which is UNIPROT Entry Names for the provided interactomes. If you provide HUGO/HGNC gene names they will be converted automatically. If you upload an interactome with different identifiers, you can convert your lists to standard formats using DAVID, or HUGO.
If the sink weights represent mRNA expression data, we suggest using a protein-DNA interaction network to connect these relevant changes to the protein-interaction network. As such, we have compiled a set of protein-DNA predicted interactions using TRANSFAC motif scanning of ENCODE provided regions for various cell types. We recommend using one of these provided datasets:
Please note: The naming of the protein nodes in the edge weights file must be consistent with the naming of the protein nodes in the protein weights input file. You can convert your lists to standard formats using DAVID, or HUGO.
The SAMNet algorithm has one parameter, gamma, that controls the number of source weights included in the network. The default value of gamma is 14 but we recommend tuning the parameter to get the best distint GO terms in the DAVID analysis performed on the network
The SAMNet algorithm typically sets all edge capacities to 1. However, to make the network more compact we have implemented a hierarchical reduction of capacities of edges that are farther from the source. Specifically, when this flag is set, capacities are 1 x 10e(-1 x shortest dist to source) for each edge in the network.
Press 'Submit Job' and your job will be submitted to a queue where it will be run sequentially with other jobs. You will be directed to a unique URL where your output will be posted when it is ready. You may provide an e-mail address if you would like to receive a notification when the results are ready. The state of the queue can be viewed here
The result page (sample shown here) includes a basic visualization of the optimal SAMNet interactome using the Cytoscape Web plug-in . This visualization is provided to give users a quick look before they download output files.
All output from the SAMNet algorithm can be downloaded ofr further analysis. Tab-delimited files are available to show which inputs were found in the interactome including the Source (S1) and Sink (T1) nodes, that were added to the original network. Cytoscape files (graph structure and node/edge attributes) are available for more detailed analysis:
More detailed information and descriptions about the SAMNet method and its application in identifying key mediators of human epithelial-mesenchymal are available in ref .
Any questions or issues please contact Sara Gosline at: sgosline _at_ mit _dot_ edu.
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