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 This is a simple protocol guide for iMiRNA-SSF web-server which is a SVM-based online predictor using structure and sequence features for microRNA identification. The feature set consists of 3-gram primary sequence features, minimum free energy (MFE), p-value of randomization test (P-value),the the local triplet sequence-structure features. For more detail information, please refer to the paper. You can find the webserver at http://bioinformatics.hitsz.edu.cn/iMiRNA-SSF/index.jsp


 You can input your test data through two ways:

  1.The first way is to input pre-miRNA sequences of FASTA format in the textarea;

  2. The another way is to select the test file of FASTA format from your local computer.

 Example sequences in FASTA format can be seen in Examples

 Click on the "Predict" button to submit your test data, or click the reset button to clear out all the information just entered.


 After the analysis, the results are shown in a user-friendly format. The results are summary into a table for each sequence, which contains sequence name, primary structure (sequence), second structure, minimum free energy (MFE), p-value of randomization test (P-value), classification.
 An output example:

sequenceName hsa-let-7c MI0000064
secondStru ((.((((((..(((.(((.(((((((((((((..((.(..((...))..).))))))))))))))).))).)))..))))))))
MFE -31.6000003814697
P-value 0.008
Classification Real pre-miRNA

 The RNA secondary structures and MFE are predicted using Vienna RNA (released 2.1.6) software package with default parameters. In the predicted secondary structure, there are only two statuses for each nucleotide, paired or unpaired, indicated by brackets ("("or")") and dots ("."), respectively. The left bracket "(" means that the paired nucleotide is located near the 5'-end and can be paired with another nucleotide at the 3'-end, which is indicated by a right bracket")". The P-value is achieved by repeating 1000 times to compute the MFE for the inferred structure based on the shuffled sequence. The Classification is the result of prediction.

Copyright@By Bin Liu's lab, Beijing Institute of Technology.

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