Efficient pairwise RNA structure prediction using probabilistic alignment constraints in Dynalign

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 j...

• Type: Journal
• Year: 2007
• Where: BMCBI
• Authors: Arif Ozgun Harmanci, Gaurav Sharma, David H. Mathews
• Keywords: Alignment Constraints, BMCBI 2007, Insertion Posterior Probabilities, Prediction