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Nucleic Acids Research Advance Access published online on December 19, 2006
Nucleic Acids Research, doi:10.1093/nar/gkl943
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© 2006 The Author(s).
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Computational Biology

High sensitivity RNA pseudoknot prediction

Xiaolu Huang1,2 and Hesham Ali2,*

1Department of Pathology and Microbiology, University of Nebraska Medical Center Omaha, NE 68198, USA 2 Department of Computer Science, College of Information Science and Technology, University of Nebraska at Omaha Omaha, NE 68182, USA

*To whom correspondence should be addressed. Tel/Fax: +1 402 554 3623; Email: hesham{at}unomaha.edu

Received June 17, 2006. Revised October 19, 2006. Accepted October 20, 2006.

Most ab initio pseudoknot predicting methods provide very few folding scenarios for a given RNA sequence and have low sensitivities. RNA researchers, in many cases, would rather sacrifice the specificity for a much higher sensitivity for pseudoknot detection. In this study, we introduce the Pseudoknot Local Motif Model and Dynamic Partner Sequence Stacking (PLMM_DPSS) algorithm which predicts all PLM model pseudoknots within an RNA sequence in a neighboring-region-interference-free fashion. The PLM model is derived from the existing Pseudobase entries. The innovative DPSS approach calculates the optimally lowest stacking energy between two partner sequences. Combined with the Mfold, PLMM_DPSS can also be used in predicting complicated pseudoknots. The test results of PLMM_DPSS, PKNOTS, iterated loop matching, pknotsRG and HotKnots with Pseudobase sequences have shown that PLMM_DPSS is the most sensitive among the five methods. PLMM_DPSS also provides manageable pseudoknot folding scenarios for further structure determination.


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