Recent developments in linear-space alignment methods: A survey

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Recent developments in linear-space alignment methods: A survey (1994)

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by Kun-mao Chao , Ross C. Hardison , Webb Miller

Venue:	J. Comput. Biol
Citations:	29 - 5 self

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@ARTICLE{Chao94recentdevelopments,
    author = {Kun-mao Chao and Ross C. Hardison and Webb Miller},
    title = {Recent developments in linear-space alignment methods: A survey},
    journal = {J. Comput. Biol},
    year = {1994},
    volume = {1},
    pages = {271--291}
}

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Abstract

A dynamic-programming strategy for sequence alignment first proposed in 1975 by Dan Hirschberg can be adapted to yield a number of extremely space-efficient algorithms. Specifically, these algorithms align two sequences using only ‘‘linear space’’, i.e., an amount of computer memory that is proportional to the sum of the lengths of the two sequences being aligned. This paper begins by reviewing the basic idea, as it applies to the global (i.e., end-to-end) alignment of two DNA or protein sequences. Three of our recent extensions of the technique are then outlined. The first extension computes an optimal alignment subject to the constraint that each position, i, of the first sequence must be aligned somewhere between positions L[i] and U[i] of the second sequence, for given values of L and U. The second finds all aligned position pairs (i.e., potential columns of the alignment) that occur in an alignment whose score exceeds a given threshold. The third treats the case where each of the two sequences is allowed to be an alignment (e.g., a sequence of aligned pairs), using a sensitive scoring scheme. We also describe two linear-space methods for computing k best local (i.e., involving only a part of each sequence) alignments, where k ≥ 1. One is a linear-space version of the algorithm of Waterman and Eggert (1987), and the other is based on the strategy proposed by Wilbur and Lipman (1983). Finally, we describe programs that implement various combinations of these techniques to provide a multi-sequence alignment method that is especially suited to handling a few very long sequences. The utility of these programs is illustrated by analysis of the locus control region of the β-like globin gene cluster of several mammals.

Keyphrases

linear-space alignment method recent development sequence alignment position pair locus control region second sequence several mammal dynamic-programming strategy aligned pair protein sequence recent extension various combination first sequence optimal alignment subject linear-space version linear space dan hirschberg sensitive scoring scheme basic idea like globin gene cluster computer memory first extension multi-sequence alignment method potential column linear-space method space-efficient algorithm

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