Improved algorithms for approximate string matching (extended abstract)

Papamichail, Dimitrios P.; Papamichail, Georgios

doi:10.1186/1471-2105-10-s1-s10

Cited by 13 publications

(14 citation statements)

References 11 publications

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“…computing the slide function by comparing character by character, cf. [PP08]. In this section we propose a new approach for implementing [Ukk85] algorithm, which does not involve computing suffix trees but still avoids long parallel slides.…”

Section: Computing Edit Distance Without Using Suffix Treesmentioning

confidence: 99%

Streaming algorithms for embedding and computing edit distance in the low distance regime

Chakraborty

Goldenberg

Koucký

2016

Proceedings of the Forty-Eighth Annual ACM Symposium on Theory of Computing

104

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The edit distance is a way of quantifying how similar two strings are to one another by counting the minimum number of character insertions, deletions, and substitutions required to transform one string into the other.In this paper we study the computational problem of computing the edit distance between a pair of strings where their distance is bounded by a parameter k ≪ n. We present two streaming algorithms for computing edit distance: One runs in time O(n + k 2 ) and the other n + O(k 3 ). By writing n + O(k 3 ) we want to emphasize that the number of operations per an input symbol is a small constant. In particular, the running time does not depend on the alphabet size, and the algorithm should be easy to implement.Previously a streaming algorithm with running time O(n + k 4 ) was given in the paper by the current authors (STOC'16). The best off-line algorithm runs in time O(n + k 2 ) (Landau et al., 1998) which is known to be optimal under the Strong Exponential Time Hypothesis. ogy, pattern recognition, text processing, information retrieval and many more. The edit distance between x and y, denoted by ∆(x, y), is defined as the minimum number of character insertions, deletions, and substitutions needed for converting x into y. Due to its immense applicability, the computational problem of computing the edit distance between two given strings x and y ∈ Σ n is of prime interest to researchers in various domains of computer science. Sometimes one also requires that the algorithm finds an alignment of x and y, i.e., a series of edit operations that transform x into y.In this paper we study the problem of computing edit distance of strings when given an a priori upper bound k ≪ n on their distance. This is akin to fixed parameter tractability. Arguably, the case when the edit distance is small relative to the length of the strings is the most interesting as when comparing two strings with respect to their edit distance we are implicitly making an assumption that the strings are similar. If they are not similar the edit distance is uninformative. There are few exceptions to this rule, most notably the reduction of instances of formula satisfiability (SAT) to instances of edit distance of exponentially large strings [BI15] where the edit distance of resulting strings is close to their length. However, such instance of the edit distance problem are rather artificial. For typical applications the edit distance of the two strings is much smaller then the length of the strings. Consider for example copying DNA during cell division: Human DNA is essentially a string of about 10 9 letters from {A, C, G, T }, and due to imperfections in the copying mechanism one can expect about 50 edit operations to occur during the process. So in many applications we can be looking for a handful of edit operations in large strings.Landau et al.[LMS98] provided an algorithm that runs in time O(n + k 2 ) and uses space O(n) when size of the alphabet Σ is constant. In general the running time of the algorithm given in [LMS98] is O(n · mi...

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Section: Computing Edit Distance Without Using Suffix Treesmentioning

confidence: 99%

Streaming algorithms for embedding and computing edit distance in the low distance regime

Chakraborty

Goldenberg

Koucký

2016

Proceedings of the Forty-Eighth Annual ACM Symposium on Theory of Computing

104

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“…Nonetheless, some related results and reference details could be found in [9,10,11,12,13]. The papers [9,11] present few versions of well-known algorithms to support the search, identification and sorting of the strings found in a text (see also [15]). Rather good and comprehensive survey of the methods and their software implementations is provided in [10].…”

Section: Introductionmentioning

confidence: 99%

New Error Tolerant Method for Search of Long Repeats in DNA Sequences

Tsarëv

Sadovsky

2016

Algorithms for Computational Biology

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A new method to identify all sufficiently long repeating substrings in one or several symbol sequences is proposed. The method is based on a specific gauge applied to symbol sequences that guarantees identification of the repeating substrings. It allows the matching of substrings to contain a given level of errors. The gauge is based on the development of a heavily sparse dictionary of repeats, thus drastically accelerating the search procedure. Some genomic applications illustrate the method.This paper is the extended and detailed version of the presentation at the 3 rd International Conference on Algorithms for Computational Biology to be held at Trujillo, Spain, June 21-22, 2016.

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“…In bioinformatics, the main problem of the computational biology, text processing and pattern recognition is string matching. Many algorithms have been designed to overcome this problem to make a comparison between two strings or evaluate the longest common subsequence that two string share [1].There are two types of Bioinformatics algorithms:-(a)dynamic algorithms such as Needleman-Wunsch and Smith-Waterman algorithms, these algorithms has advantages and disadvantages. The advantage :-finds the optimal alignment solution between the sequences, the disadvantage:-takes more time to make the alignment this decrease the performance.…”

Section: Introductionmentioning

confidence: 99%

Fast Dynamic Algorithm for Sequence Alignment Based On Bioinformatics

Shehab¹,

Keshk²,

Mahgoub³

2012

IJCA

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Sequence alignment is widely used in Bioinformatics for Genome Sequence difference identification. It is the main problem of computational biology. Any sequence of Deoxyribonucleic acid (DNA), Ribonucleic acid (RNA), and protein can be alignment by many algorithms called bioinformatics algorithms. This paper presents a new implemented algorithm for sequence alignment based on concepts from bioinformatics algorithms .The implemented algorithm is called fast dynamic algorithm for sequence alignment (FDASA). This implemented algorithm based on making a matrix of M×N (M is the length of the first sequence, N is the length of the second sequence), After that filling the three main diagonal without filling the unused data and at the same time get the optimal solution; so that the execution time is decreased, the performance is high and the memory location decreased. The implementation introduced in this paper made a comparison between the dynamic algorithms Needleman-Wunsch algorithm, Smith-Waterman and our algorithm FDASA to test the execution time. The results show that our algorithm FDASA decreased the execution time when compared with Needleman-Wunsch and Smith-Waterman algorithms.

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Improved algorithms for approximate string matching (extended abstract)

Cited by 13 publications

References 11 publications

Streaming algorithms for embedding and computing edit distance in the low distance regime

Streaming algorithms for embedding and computing edit distance in the low distance regime

New Error Tolerant Method for Search of Long Repeats in DNA Sequences

Fast Dynamic Algorithm for Sequence Alignment Based On Bioinformatics

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