A Subquadratic Sequence Alignment Algorithm for Unrestricted Scoring Matrices

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Abstract
International audienceGiven two strings of size n over a constant alphabet, the classical algorithm for computing the similarity between two sequences [D. Sankoff and J. B. Kruskal, eds., Time Warps, String Edits, and Macromolecules; Addison-Wesley, Reading, MA, 1983; T. F. Smith and M. S. Waterman, J. Molec. Biol., 147 (1981), pp. 195-197] uses a dynamic programming matrix and compares the two strings in O(n²) time. We address the challenge of computing the similarity of two strings in subquadratic time for metrics which use a scoring matrix of unrestricted weights. Our algorithm applies to both local and global similarity computations. The speed-up is achieved by dividing the dynamic programming matrix into variable sized blocks, as induced by Lempel-Ziv parsing of both strings, and utilizing the inherent periodic nature of both strings. This leads to an O(n² / log n) algorithm for an input of constant alphabet size. For most texts, the time complexity is actually O(h n² / log n), where h≤1 is the entropy of the text. We also present an algorithm for comparing two run-length encoded strings of length m and n, compressed into m' and n' runs, respectively, in O(m'n+n'm) complexity. This result extends to all distance or similarity scoring schemes that use an additive gap penalty

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