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 Similarity

miRNA Functional Similarity (miRFun)

     1. About miRFun

     2. How to use miRFun

         1. input

           2. Target prediction methods

           3. Cut-off for target prediction

           4. Targetnetwork

     3. Result page interpretation

1. About miRFun

       miRFun is a method to compute the functional similatiy of Soybean (Glycine max.) miRNAs by taking fully into account both the functional similarity between their targets and the regulatory strengths they exert on their targets.

      miRFun firstly measures the regulatory strength based on site accessibility and the functional similarity between the target genes of two miRNAs based on a gene functional network (GFN). And further infer the functional similarity for these two miRNAs by integrating these two critical factors. The workflow of miRFun is shown as Figure 1.

miRFun Workflow miRNA Target Prediction Target Functional Network miRNA Functional similarity miRNA Functional similarity

Figure 1. A schematic view of the workflow for measuring the functional similarity of two miRNAs.

2.How to use miRFun?

      (1) Input

       Input or paste the miRNAs into the text box(s).Select a way you want to input your miRNAs, such as "List","Pairs" or "Two Lists". Click "Sample input" to see the input format.


      (2) Target prediction methods

       Due to the lack of experimental targets of soybean miRNAs, in silico methods for miRNA target prediction are used in this study. To reduce the biases introduced by individual predicting method, we incorporate all miRNA-target interactions that occur in three accepted predicting methods using their default settings respectively. The three methods are

      PITA (

      psRNATarget (

      TAPIR (; using precise search).

Select at least one method for target predition. It will return the targets that occur in all methods you selected!


      (3) Cut-off for target prediction

      Since the highly complementary sequence feature of miRNA-target duplexes in plants, to reduce the false positive, you need to set an appropriate criteria to reduce false positives in the target prediction according to your needs.

      NOTE:You are strongly   recommended to use the default settings, under which all pairwise miRNAs'   similarity have been precalculated. Any changes will start a new calculation   and may extend the time to wait for the results.

       Target accessibility ()

       The accessibility of mRNA target site to small RNA has been identified as one of important factors that are involved in target recognition because the secondary structure (stem etc.) around target site will prevent small RNA (including miRNA and ta-siRNA, sic passim) and mRNA target from contacting. The psRNATarget server employes RNAup to calculate target accessbility, which is represented by the energy required to open (unpair) secondary structure around target site (usually the complementary region with small RNA and up/downstream) on target mRNA(see figure below). The less energy means the more possibility that small RNA is able to contact (and cleave) target mRNA.

       In above figure, represents the energy that is required to open secondary structure around target site. We use a software, namely RNAup, described by Muckstein et al (2005, pmid=16446276) to calculate this value.

      (4)Target network
      Select an target network from three typs, which was contructed   under the GO Annotation of BP,MF and CC, respectively.

      And then click "Submit"   to compute.

3. Results page interpretation

       After computation, the rusult page as follow will be shown in the same page.
       A: The number of the pairwise functional similarity of miRNAs you intput.

       B: Click to download the results you selected.

       C: Click to select all lines of the result table.

       D: The first miRNAs of each miRNA pair.

       E: The second miRNAs of each miRNA pair.

       F: The functional similarity of the two miRNAs in the left two cells.

       G: The co-rugulation coefficient (w) of the two miRNAs.

       H: The functional similarity of their target genes based on target gene network.

       I: The number of targets of first miRNAs. Click the number to go to the detail infomation of its target genes.

       J: The number of targets of first miRNAs. Click the number to go to the detail infomation of its target genes.

       K: The number of the common target of the two miRNAs. Click the number to go to the detail infomation of thier target genes.

       L: The GO ontology of the target network you selected.

       M: Click to select the result of this line.

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