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Title

RNA-SSPT: RNA Secondary Structure Prediction Tools

 

Authors

Freed Ahmad, Shahid Mahboob, Tahsin Gulzar, Salah U din, Tanzeela Hanif, Hifza Ahmad & Muhammad Afzal*

 

Affiliation

Department of Bioinformatics and Biotechnology, G C University, Faisalabad, Pakistan

 

Email

afzalarsenal@googlemail.com; *Corresponding author

 

Article Type

Software

 

Date

Received September 16, 2013; Accepted September 16, 2013; Published October 16, 2013

 

Abstract

The prediction of RNA structure is useful for understanding evolution for both in silico and in vitro studies. Physical methods like NMR studies to predict RNA secondary structure are expensive and difficult. Computational RNA secondary structure prediction is easier. Comparative sequence analysis provides the best solution. But secondary structure prediction of a single RNA sequence is challenging. RNA-SSPT is a tool that computationally predicts secondary structure of a single RNA sequence. Most of the RNA secondary structure prediction tools do not allow pseudoknots in the structure or are unable to locate them. Nussinov dynamic programming algorithm has been implemented in RNA-SSPT. The current studies shows only energetically most favorable secondary structure is required and the algorithm modification is also available that produces base pairs to lower the total free energy of the secondary structure. For visualization of RNA secondary structure, NAVIEW in C language is used and modified in C# for tool requirement. RNA-SSPT is built in C# using Dot Net 2.0 in Microsoft Visual Studio 2005 Professional edition. The accuracy of RNA-SSPT is tested in terms of Sensitivity and Positive Predicted Value. It is a tool which serves both secondary structure prediction and secondary structure visualization purposes.

 

Keywords

RNA secondary structure prediction, C#, Nussinov algorithm, dot net

 

Citation

Ahmad et al.  Bioinformation 9(17): 873-878 (2013)

 

Edited by

P Kangueane

 

ISSN

0973-2063

 

Publisher

Biomedical Informatics

 

License

This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.