Iterative estimation of structures of multiple RNA homologs: Turbofold

Abstract

TurboFold, an iterative algorithm for estimating the common secondary structures of multiple RNA homologs, is presented. The algorithm is motivated by and has structure and attributes analogous to the turbo decoding algorithm in communications. Instead of solving the joint problem of aligning and folding multiple RNA sequences, TurboFold uses an iterative process to fold a collection of RNA homologs. Beneficial information from inter-sequence comparisons is incorporated by using feedback from iteration to iteration in the form of pseudo-prior probabilities for base pairing which are incorporated in the computation of base pairing probabilities. As a result TurboFold retains several of the advantages of join alignment and folding while maintaining a per iteration computational complexity comparable to single sequence RNA folding. Experimental evaluation of the algorithm, performed over six ncRNA families, demonstrates that TurboFold achieves high accuracy, offering better performance than available alternatives for estimating RNA base pairing probabilities.Index Terms-RNA secondary structure, Turbo decoding, Iterative Estimation INTRODUCTION AND BACKGROUNDRNA has recently emerged as a key player in biology, serving a number of crucial noncoding roles, in addition to its traditionally known function as a transient mRNA copy of the genetic code for protein synthesis [1]. The secondary structure, i.e. the set of hydrogenbond-mediated pairings between bases located within the linear strand of an RNA molecule, plays a key role in defining the function for these noncoding RNAs (ncRNAs). The secondary structure is important in developing hypotheses about function, finding new instances of an RNA family in genomes , designing therapeutics , and is crucial in modeling and solving the 3D structure . Computational prediction of RNA secondary structure, commonly referred to as computational RNA folding, is therefore an important problem in computational biology [2, Chap. 10].Computational methods for predicting RNA secondary structure use, as inputs, the primary structure information for RNA, which, for the purposes of the ensuing discussion, consists of a sequence x ∈ A N where A = {A, U, G, C} and N denotes the length of the sequence. The length of the sequence corresponds to the number of nucleotides in the RNA chain and the elements of the sequence denote the identifying nitrogenous base associated with each of the nucleotides arranged in order from the 5 to the 3 end of the chain.

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