Computing similarity between RNA strings

TLDR: This paper defines a notion of alignment between two RNA strings and presents a method for optimally aligning a given RNA sequence with unknown secondary structure to one with known sequence and structure, thus attacking the structure prediction problem in the case when the structure of a closely related sequence is known.

Abstract: Ribonucleic acid (RNA) strings are strings over the four-letter alphabet {A,C,G,U} with a secondary structure of base-pairing between A-U and C-G pairs in the string Edges are drawn between two bases that are paired in the secondary structure and these edges have traditionally been assumed to be noncrossing The noncrossing base-pairing naturally leads to a tree-like representation of the secondary structure of RNA strings In this paper, we address several notions of similarity between two RNA strings that take into account both the primary sequence and secondary base-pairing structure of the strings We present efficient algorithms for exact matching and approximate matching between two RNA strings We define a notion of alignment between two RNA strings and devise algorithms based on dynamic programming We then present a method for optimally aligning a given RNA sequence with unknown secondary structure to one with known sequence and structure, thus attacking the structure prediction problem in the case when the structure of a closely related sequence is known The techniques employed to prove our results include reductions to well-known string matching problems, allowing wild cards and ranges, and speeding up dynamic programming by using the tree structures implicit in the secondary structure of RNA strings