Sparse dynamic programming I

Eppstein, David; Galil, Zvi; Giancarlo, Raffaele; Italiano, Giuseppe F.

doi:10.1145/146637.146650

Cited by 126 publications

(119 citation statements)

References 27 publications

Supporting

Mentioning

118

Contrasting

Order By: Relevance

“…Chao and Miller (1994) combine a time-efficient algorithm developed by Eppstein et al (1992) with a space-saving approach of Hirschberg (1975) to obtain an algorithm that uses O ((M + N + F log N )log M ) time and O (M + N ) space to deliver the best chain of fragments from a pool of F fragments. They then give a time-and-space efficient algorithm to compute n best non-intersecting chains of fragments following the general outline of Huang and Miller (1991).…”

Section: Building N Best Non-intersecting Chains Of Fragmentsmentioning

confidence: 99%

A local alignment tool for very long DNA sequences

et al. 1995

View full text Add to dashboard Cite

Abstract. This paper presents a practical program, called sim2, for building local alignments of two sequences, each of which may be hundreds of kilobases long. Sim2 first constructs n best non-intersecting chains of ''fragments,'' such as all occurrences of identical 5-tuples in each of two DNA sequences, for any specified n ≥ 1. Each chain is then refined by delivering an optimal alignment in a region delimited by the chain. Sim2 requires only space proportional to the size of the input sequences and the output alignments, and the same source code runs on UNIX machines, on Macintosh, on PC, and on DEC ALPHA PC. We also describe an application of sim2 for aligning long DNA sequences from E. coli. Sim2 facilitates contig-building by providing a complete view of the related sequences, so differences can be analyzed and inconsistencies resolved. Examples are shown using the alignment display and editing functions from the software tool, ChromoScope.

show abstract

Section: Building N Best Non-intersecting Chains Of Fragmentsmentioning

confidence: 99%

A local alignment tool for very long DNA sequences

et al. 1995

View full text Add to dashboard Cite

show abstract

“…Eppstein et al [11], [12] modify the objective function for speedup. Landau et al [20] devised an alignment algorithm that runs in subquadratic time by exploiting the compressibility of typical input sequences.…”

Section: Introductionmentioning

confidence: 99%

Faster Algorithms for Optimal Multiple Sequence Alignment Based on Pairwise Comparisons

Bilu

Agarwal²,

Kolodny

2006

IEEE/ACM Trans. Comput. Biol. and Bioinf.

View full text Add to dashboard Cite

Abstract-Multiple Sequence Alignment (MSA) is one of the most fundamental problems in computational molecular biology. The running time of the best known scheme for finding an optimal alignment, based on dynamic programming, increases exponentially with the number of input sequences. Hence, many heuristics were suggested for the problem. We consider a version of the MSA problem where the goal is to find an optimal alignment in which matches are restricted to positions in predefined matching segments. We present several techniques for making the dynamic programming algorithm more efficient, while still finding an optimal solution under these restrictions. We prove that it suffices to find an optimal alignment of the predefined sequence segments, rather than single letters, thereby reducing the input size and thus improving the running time. We also identify "shortcuts" that expedite the dynamic programming scheme. Empirical study shows that, taken together, these observations lead to an improved running time over the basic dynamic programming algorithm by 4 to 12 orders of magnitude, while still obtaining an optimal solution. Under the additional assumption that matches between segments are transitive, we further improve the running time for finding the optimal solution by restricting the search space of the dynamic programming algorithm.

show abstract

“…These three edit operations are the ones commonly used in applications [EGGI92,SK83,WF74,WM92], though only insertions and deletions are considered in some work [My86]. A discrepancy between A and B that is corrected by an edit operation is called a difference.…”

Section: Introductionmentioning

confidence: 99%

Efficient algorithms for approximate string matching with swaps

Lee

Kim

Park

et al. 1997

Combinatorial Pattern Matching

View full text Add to dashboard Cite

Most research on the edit distance problem and the k-differences problem considered the set of edit operations consisting of changes, insertions, and deletions. In this paper we include the swap operation that interchanges two adjacent characters into the set of allowable edit operations, and we present an O(t min(m, n))-time algorithm for the extended edit distance problem, where t is the edit distance between the given strings, and an O(kn)-time algorithm for the extended k-differences problem. That is, we add swaps into the set of edit operations without increasing the time complexities of previous algorithms that consider only changes, insertions, and deletions for the edit distance and k-differences problems. 1999Academic Press

show abstract

Sparse dynamic programming I

Cited by 126 publications

References 27 publications

A local alignment tool for very long DNA sequences

A local alignment tool for very long DNA sequences

Faster Algorithms for Optimal Multiple Sequence Alignment Based on Pairwise Comparisons

Efficient algorithms for approximate string matching with swaps

Contact Info

Product

Resources

About