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 miRNA Functional Network

miRNA Functional Network (miRFN)

     1. About miRFN

     2. How to use miRFN

         1. input

           2. Aspect of Network

           3. Weight threshold

           4. Max path length

     3. Result page interpretation


1. About miRFun

       miRFN is a garaphic view tool for miRNA functional networks of Soybean (Glycine max.), which are contructed based on the functional similarity of miRNAs.

     

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


2.How to use miRFun?

      (1) Input

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

      (2) Aspect of Networks

       Due to there are three types of target gene network based one three GO aspects, we totally constructed foure miRNA functional networks. They are Biological Process (BP), Molecular Function(MF), Cellular Component (CC), and Integration of all above. The summary properties of soybean miRNA functional networks in BP, MF, CC, and Integration are shown in the table bellow.

Property

BP

MF

CC

Integration

Number of Nodes

462

454

512

472

Number of Edges

7858

8271

16813

7038

Cluster Coefficient

0.782

0.779

0.832

0.762

Connected components

13

15

5

11

Diameter

6

5

6

7

Radius

1

1

1

1

Centralization

0.503

0.467

0.511

0.487

Shortest paths

185394

162316

253520

193276

Characteristic path length

2.372

2.287

2.104

2.573

Avg. number of neighbors

34.017

36.436

65.676

29.822

Density

0.074

0.080

0.129

0.063

Heterogeneity

1.281

1.292

1.135

1.323

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

 

      (3) Weight threshold

      A miRNA functional network here is a weighted undirected graph that miRNAs represent the nodes and their functional interactions represent the edges, which are weighted by the pairwise functional similarities of miRNAs they linked. You should set an appropriate threshold to ensure that miRNA 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 miRNAs that have the ditance of this value to the seed miRANs 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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