Efficient pairwise RNA structure prediction using probabilistic alignment constraints in Dynalign |
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Authors: | Arif Ozgun Harmanci Gaurav Sharma David H Mathews |
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Institution: | (1) Department of Electrical and Computer Engineering, University of Rochester, Hopeman 204, RC Box 270126, Rochester, NY 14627, USA;(2) Department of Biostatistics and Computational Biology, University of Rochester Medical Center, 601 Elmwood Avenue, Box 630, Rochester, NY 14642, USA;(3) Department of Biochemistry and Biophysics, University of Rochester Medical Center, 601 Elmwood Avenue, Box 712, Rochester, NY 14642, USA |
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Abstract: | Background Joint alignment and secondary structure prediction of two RNA sequences can significantly improve the accuracy of the structural
predictions. Methods addressing this problem, however, are forced to employ constraints that reduce computation by restricting
the alignments and/or structures (i.e. folds) that are permissible. In this paper, a new methodology is presented for the
purpose of establishing alignment constraints based on nucleotide alignment and insertion posterior probabilities. Using a
hidden Markov model, posterior probabilities of alignment and insertion are computed for all possible pairings of nucleotide
positions from the two sequences. These alignment and insertion posterior probabilities are additively combined to obtain
probabilities of co-incidence for nucleotide position pairs. A suitable alignment constraint is obtained by thresholding the co-incidence probabilities.
The constraint is integrated with Dynalign, a free energy minimization algorithm for joint alignment and secondary structure
prediction. The resulting method is benchmarked against the previous version of Dynalign and against other programs for pairwise
RNA structure prediction. |
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