A Space-Economical Suffix Tree Construction Algorithm

McCreight, Edward M.

doi:10.1145/321941.321946

Cited by 1,229 publications

(669 citation statements)

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“…For formal descriptions, the readers are referred to [6,10]. McCreight [18] proposed a linear-time construction algorithm using auxiliary links called suffix links and also an algorithm for an incremental editing, which transform the suffix tree of S = αβγ to that of S ′ = αδγ for some (possibly empty) string α, β, δ, γ.…”

Section: Suffix Treesmentioning

confidence: 99%

“…The suffix tree of a string S is a compacted trie representing all suffixes of S [18,22]. Over the years, the suffix tree has not only been a fundamental data structure in the area of string algorithms but also it has been used for many applications in engineering and computational biology.…”

Section: Introductionmentioning

confidence: 99%

“…Over the years, the suffix tree has not only been a fundamental data structure in the area of string algorithms but also it has been used for many applications in engineering and computational biology. The suffix tree can be constructed in O(|S|) time for a constant alphabet [18,21] and an integer alphabet [8], where |S| denotes the length of S. The suffix tree has |S| leaves and requires O(|S|) space.…”

Section: Introductionmentioning

confidence: 99%

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Suffix Tree of Alignment: An Efficient Index for Similar Data

Park

Crochemore

et al. 2013

Lecture Notes in Computer Science

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Abstract. We consider an index data structure for similar strings. The generalized suffix tree can be a solution for this. The generalized suffix tree of two strings A and B is a compacted trie representing all suffixes in A and B. It has |A|+|B| leaves and can be constructed in O(|A|+|B|) time. However, if the two strings are similar, the generalized suffix tree is not efficient because it does not exploit the similarity which is usually represented as an alignment of A and B. In this paper we propose a space/time-efficient suffix tree of alignment which wisely exploits the similarity in an alignment. Our suffix tree for an alignment of A and B has |A|+l d +l1 leaves where l d is the sum of the lengths of all parts of B different from A and l1 is the sum of the lengths of some common parts of A and B. We did not compromise the pattern search to reduce the space. Our suffix tree can be searched for a pattern P in O(|P | + occ) time where occ is the number of occurrences of P in A and B. We also present an efficient algorithm to construct the suffix tree of alignment. When the suffix tree is constructed from scratch, the algorithm requires O(|A| + l d + l1 + l2) time where l2 is the sum of the lengths of other common substrings of A and B. When the suffix tree of A is already given, it requires O(l d + l1 + l2) time.

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Section: Suffix Treesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Suffix Tree of Alignment: An Efficient Index for Similar Data

Park

Crochemore

et al. 2013

Lecture Notes in Computer Science

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“…It is a generalization of the well-known suffix tree of a set of sequences [65,16], which is one of the most important data structures in the field of pattern recognition. Such a suffix tree has O(n 1 + · · · + n k ) nodes and can be constructed in O(n 1 + · · · + n k ) time by several algorithms [65,30,58,12,16].…”

Section: Suffix-set Treesmentioning

confidence: 99%

Multiple Sequence Alignment Based on Set Covers

Porto

Barbosa

2006

Lecture Notes in Computer Science

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We introduce a new heuristic for the multiple alignment of a set of sequences. The heuristic is based on a set cover of the residue alphabet of the sequences, and also on the determination of a significant set of blocks comprising subsequences of the sequences to be aligned. These blocks are obtained with the aid of a new data structure, called a suffix-set tree, which is constructed from the input sequences with the guidance of the residue-alphabet set cover and generalizes the well-known suffix tree of the sequence set. We provide performance results on selected BAliBASE amino-acid sequences and compare them with those yielded by some prominent approaches.

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“…After all, representing the O(n 2 ) substrings of a text in O(n) space is a remarkably powerfull tool. Even more advantageous is that suffix trees can be constructed in linear time [Weiner 1973;McCreight 1976;Ukkonen 1995].…”

Section: Introductionmentioning

confidence: 99%

Engineering a Compressed Suffix Tree Implementation

Välimäki

Gerlach

Dixit

et al. 2007

Experimental Algorithms

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Suffix tree is one of the most important data structures in string algorithms and biological sequence analysis. Unfortunately, when it comes to implementing those algorithms and applying them to real genomic sequences, often the main memory size becomes the bottleneck. This is easily explained by the fact that while a DNA sequence of length n from alphabet Σ = {A, C, G, T } can be stored in n log |Σ| = 2n bits, its suffix tree occupies O(n log n) bits. In practice, the size difference easily reaches factor 50.We report on an implementation of the compressed suffix tree very recently proposed by Sadakane (Theory of Computing Systems, in press). The compressed suffix tree occupies space proportional to the text size, i.e. O(n log |Σ|) bits, and supports all typical suffix tree operations with at most log n factor slowdown. Our experiments show that, e.g. on a 10 MB DNA sequence, the compressed suffix tree takes 10% of the space of the normal suffix tree. At the same time, a representative algorithm is slowed down by factor 30.Our implementation follows the original proposal in spirit, but some internal parts are tailored towards practical implementation. Our construction algorithm has time requirement O(n log n log |Σ|) and uses closely the same space as the final structure while constructing it: on the 10 MB DNA sequence, the maximum space usage during construction is only 1.5 times the final product size. As by-products, we develop a method to create Succinct Suffix Array directly from Burrows-Wheeler transform and a space-efficient version of suffixes-insertion algorithm to build balanced parentheses representation of suffix tree from LCP information.

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A Space-Economical Suffix Tree Construction Algorithm

Cited by 1,229 publications

References 1 publication

Suffix Tree of Alignment: An Efficient Index for Similar Data

Suffix Tree of Alignment: An Efficient Index for Similar Data

Multiple Sequence Alignment Based on Set Covers

Engineering a Compressed Suffix Tree Implementation

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