A fast and practical bit-vector algorithm for the Longest Common Subsequence problem

Crochemore, Maxime; Iliopoulos, Costas S.; Pinzón, Yoan J.; Reid, James F.

doi:10.1016/s0020-0190(01)00182-x

Cited by 69 publications

(70 citation statements)

References 12 publications

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“…It is important to observe that in all cases even ED 2 .x W y/ is significantly better than either H.x W y/, ED.x W y/, or ID.x W y/ currently in use. generalizations of the edit and insertion-deletion distances, and can be implemented in a computationally efficient way by means of the bit-vector approach to computing a dynamic programming matrix (Allison and Dix, 1986;Myers, 1999;Crochemore et al, 2001;Hyyrö et al, 2005).…”

Section: Hamming Edit and Insertion-deletion Similaritymentioning

confidence: 99%

New, Improved, and Practical k-Stem Sequence Similarity Measures for Probe Design

Macula

Schliep

Bishop

et al. 2008

Journal of Computational Biology

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We define new measures of sequence similarity for oligonucleotide probe design. These new measures incorporate the nearest neighbor k-stem motifs in their definition, but can be efficiently computed by means of a bit-vector method. They are not as computationally costly as algorithms that predict nearest neighbor hybridization potential. Our new measures for sequence similarity correlate significantly better with nearest neighbor thermodynamic predictions than either BLAST or the standard edit or insertion-deletion defined similarities already in use in many different probe design applications.

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Section: Hamming Edit and Insertion-deletion Similaritymentioning

confidence: 99%

New, Improved, and Practical k-Stem Sequence Similarity Measures for Probe Design

Macula

Schliep

Bishop

et al. 2008

Journal of Computational Biology

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“…Given two n-sequences x, y, all of our programs use a "folklore" dynamic programming algorithm to find the lcs(x,y) . This is essentially described in [10]. The complexity of this subroutine is O(n 2 ) .…”

Section: Dna(nd) Code Generation Programsmentioning

confidence: 99%

“…The complexity of this subroutine is O(n 2 ) . In [10], an improvement of the "folklore" algorithm is given and we plan to incorporate this in our future programs.…”

Section: Dna(nd) Code Generation Programsmentioning

confidence: 99%

DNA Tag-Antitags (TAT) Codes

Macula¹

2003

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Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing this collection of information. DNA TAG-ANTITAGS (TAT) CODES AUTHOR(S)Anthony J. Macula FUNDING NUMBERSC -F30602-02-2-0205 PE -61102F PR -DNAT TA -AT WU -02 PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) 12b. DISTRIBUTION CODE ABSTRACT (Maximum 200 Words)This research addresses the initial stages of the development of an enabling technology for DNA computing and other biological assay applications. This work combines mathematics, computer science and chemistry. It is focused on the construction of a biomolecular architecture designed to employ new algorithmic paradigms based on the massively parallel computational power of DNA hybridization. The ultimate intent is to develop a computing basis to eventually overcome the exponential time complexity of many discrete math problems so that they can be solved in linear real time. Many of these computationally hard (NP) problems are critical to logistics, scheduling and security. In particular, we made an initial application of biomolecular computing methods to data mining. Data mining has important applications to information security, assurance and superiority. In this research, we developed methods of generating large collections of single stranded DNA sequences called a DNA (n,d)code. DNA(n,d) codes serve as universal components for biomolecular computing. DNA(n,d) codes are closed under reverse-complementation. The strands in a DNA(n,d) code have such binding specificity that a code strand will only hybridize with its reverse-complement and will not cross hybridize with any other code strand in the DNA(n,d) code. Such collections of strands are crucial to the success of DNA computing. NUMBER OF PAGES 23

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“…A method to compute the LLCS faster with bitparallelism is well-known. This method requires O( m/w n) time and O(m + n) space [2], where w is the word size of a computer. Using this method, Hirschberg's LCS algorithm can be accelerated.…”

Section: Introductionmentioning

confidence: 99%

A GPU Implementation of a Bit-parallel Algorithm for Computing the Longest Common Subsequence

Kawanami

Fujimoto

2014

IPSJ Online Transactions

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Abstract:The longest common subsequence (LCS) for two given strings has various applications, such as for the comparison of deoxyribonucleic acid (DNA). In this paper, we propose a graphics processing unit (GPU) algorithm to accelerate Hirschberg's LCS algorithm improved with Crochemore et al.'s bit-parallel algorithm. Crochemore et al.'s algorithm includes bitwise logical operators, which can be computed easily in parallel because they have bitwise parallelism. However, Crochemore et al.'s algorithm also includes an operator with less parallelism, i.e., an arithmetic sum. In this paper, we focus on how to implement these operators efficiently in parallel and experimentally show the following results. First, the proposed GPU algorithm with a 2.67 GHz Intel Core i7 920 CPU and GeForce GTX 580 GPU performs a maximum of 12.81 times faster than the bit-parallel CPU algorithm using a single-core 2.67 GHz Intel Xeon X5550 CPU. Subsequently, the proposed GPU algorithm executes a maximum of 4.56 times faster than the bit-parallel CPU algorithm using a four-core 2.67 GHz Intel Xeon X5550 CPU. Furthermore, the proposed algorithm with GeForce 8800 GTX performs 10.9 to 18.1 times faster than Kloetzli et al.'s existing GPU algorithm with the same GPU.

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A fast and practical bit-vector algorithm for the Longest Common Subsequence problem

Cited by 69 publications

References 12 publications

New, Improved, and Practical k-Stem Sequence Similarity Measures for Probe Design

New, Improved, and Practical k-Stem Sequence Similarity Measures for Probe Design

DNA Tag-Antitags (TAT) Codes

A GPU Implementation of a Bit-parallel Algorithm for Computing the Longest Common Subsequence

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