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© Oxford University Press

Reduced space sequence alignment

J.Alicia Grice , Richard Hughey 1 and Don Speck

Computer Engineering, University of California Santa Cruz, CA 95064, USA

1To whom correspondence should be addressed

Motivation: Sequence alignment is the problem of finding the optimal character-by-character correspondence between two sequences. It can be readily solved in O(n2) time and O(n2) space on a serial machine, or in O(n) time with O(n) space per O(n) processing elements on a parallel machine. Hirschberg's divide-and-conquer approach for finding the single best path reduces space use by a factor of n while inducing only a small constant slowdown to the serial version.

Results: This paper presents a family of methods for computing sequence alignments with reduced memory that are well suited to serial or parallel implementation. Unlike the divide-and-conquer approach, they can be used in the forward-backward (Baum-Welch) training of linear hidden Markov models, and they avoid data-dependent repartitioning, making them easier to parallelize. The algorithms feature, for an arbitrary integer L, a factor proportional to L slowdown in exchange for reducing space requirement from O(n2) to O(n). A single best path member of this algorithm family matches the quadratic time and linear space of the divide-and-conquer algorithm. Experimentally, the O(n1.5)-space member of the family is 15–40% faster than the O(n)-space divide-and-conquer algorithm.

Availability: The methods will soon be incorporated in the SAM hidden Markov modeling package http: //www.cse.ucs-c.edu/research/compbio/sam.html.

Contact: wzrph{at}cse.ucsc.edu

Received on July 8, 1996; accepted on September 9, 1996

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