Soybean miRNA Functional Network

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This site will no longer be updated and maintained from now on. The current services will continue to be provided and updated at the new database: SoyFN. We apologize for the inconvenience.

About Target Functional Gene Network

Target Functional Gene Network (TarFGN)

     1. About TarFGN

     2. How to use TarFGN

         1. input

           2. Aspect of Network

           3. Weight threshold

           4. Max path length

     3. Result page interpretation


1. About TarFGN

       TarFGN is a garaphic view tool for miRNA target functional gene networks of Soybean (Glycine max.), which are contructed based on the functional similarity of gene using GO semantic similarity.

     

Figure 1.A graphic view of the integrated soybean miRNA network.


2.How to use FGN?

      (1) Input

       Input or paste the seed genes into the text box(s). one per row. Click "Sample input" to see example.

      (2) Aspect of Networks

       Due to there are three GO aspects, we totally constructed three functional gene networks based on pairwise functional similarity upon GO semantic similarity. They are Biological Process (BP), Molecular Function(MF), and Cellular Component (CC).

      So you need to select a network you want to search in!

 

      (3) Weight threshold

      A functional gene network here is a weighted undirected graph that genes represent the nodes and their functional interactions represent the edges, which are weighted by the pairwise functional similarities ofgenes they linked. You should set an appropriate threshold to ensure that gene pairs with functional similarities greater than or equal to the threshold will be connected by edges; otherwise, they are not connected directly. The recommended thresholds were set using clustering coefficient-based threshold selection.

           

      (4)Max length
      The max length parameter is used to determine where other genes that have the ditance of this value to the seed genes be brought into the network. If it is 0, the output network will only contain the nodes you input and the edges between them. While it is 1, it will contain their first neighbours and edeges between them, and so on. The bigger the value, the more time-consuming.

      And then click "Submit"   to get the network.


3. Results page interpretation

       After computation, the rusult page as follow will be shown in the same page.


     

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