Efficient Algorithms for Handling Molecular Weighted Sequences

Iliopoulos, Costas S.; Makris, Christos; Panagis, Yannis; Perdikuri, Katerina; Theodoridis, Evangelos; Tsakalidis, Athanasios K.

doi:10.1007/1-4020-8141-3_22

Cited by 11 publications

(11 citation statements)

References 15 publications

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“…Regarding the p-weighted sequences, Iliopoulos et al [8] defined the problem of longest common substring of p-weighted sequences, where the common sequence is consecutive. They suggested solving the problem using a p-weighted generalized suffix tree, in which the longest branch common to both strings is the answer.…”

Section: Related Workmentioning

confidence: 99%

Weighted LCS

Amir

Gotthilf

Shalom

2010

Journal of Discrete Algorithms

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The Longest Common Subsequence (LCS) of two strings A, B is a well studied problem having a wide range of applications. When each symbol of the input strings is assigned a positive weight the problem becomes the Heaviest Common Subsequence (HCS) problem. In this paper we consider a different version of weighted LCS on Position Weight Matrices (PWM). The Position Weight Matrix was introduced as a tool to handle a set of sequences that are not identical, yet, have many local similarities. Such a weighted sequence is a 'statistical image' of this set where we are given the probability of every symbol's occurrence at every text location. We consider two possible definitions of LCS on PWM.For the first, we solve the LCS problem of z sequences in time O (zn z+1 ). For the second, we consider the log-probability version of the problem, prove N P-hardness and provide an approximation algorithm.

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Section: Related Workmentioning

confidence: 99%

Weighted LCS

Amir

Gotthilf

Shalom

2010

Journal of Discrete Algorithms

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“…The WST was firstly presented in [7] as an elegant data structure for reporting the repetitions within a weighted biological sequence. In [8] authors presented an efficient algorithm for constructing the WST, while in [25] authors present several applications of the WST. A more formal definition of the WST follows.…”

Section: Definitionmentioning

confidence: 99%

“…2). For the construction of gWST(S) the algorithm of [8] is used for each of the weighted sequences in S and all the produced factors are superimposed in the same compacted trie. The total time for this operation is linear to the sum of the length of each of the weighted sequences O( N i=1 |s i |) = O(nN).…”

Section: Extracting Simple Modelsmentioning

confidence: 99%

“…Notice that subword S 9,1 = T T T is inserted by S 8,1 = AT T T and by S 8,2 = GT T T . See [8] for a detailed description of weighted suffix tree construction algorithm. Eventually, a leaf-list is a set of N vectors, one for each of the weighted sequences, where each vector contains σ + 1 lists, one for each of the σ + 1 choices for left-character (see Fig.…”

Section: Extracting Simple Modelsmentioning

confidence: 99%

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Algorithms for extracting motifs from biological weighted sequences

Iliopoulos

Perdikuri

Theodoridis

et al. 2007

Journal of Discrete Algorithms

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In this paper we present three algorithms for the Motif Identification Problem in Biological Weighted Sequences. The first algorithm extracts repeated motifs from a biological weighted sequence. The motifs correspond to repetitive words which are approximately equal, under a Hamming distance, with probability of occurrence 1/k, where k is a small constant. The second algorithm extracts common motifs from a set of N 2 weighted sequences. In this case, the motifs consists of words that must occur with probability 1/k, in 1 q N distinct sequences of the set. The third algorithm extracts maximal pairs from a biological weighted sequence. A pair in a sequence is the occurrence of the same word twice. In addition, the algorithms presented in this paper improve previous work on these problems.

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“…The problem of motifs localization in weighted sequences is also discussed in [4] where authors use the WST [3] in order to localize motifs.…”

Section: Previous Workmentioning

confidence: 99%

Motif Extraction from Biological Sequences: Trends and Contributions to Other Scientific Fields

Perdikuri

Tsakalidis

Third International Conference on Information Technology and Applications (ICITA'05)

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In this paper we present algorithms for the localization and extraction of interesting motifs from Biological Sequences. We are especially interested in Weighted Sequences, which are extensively used in Molecular Biology as profiles for protein families and for the representation of binding sites. It is our belief that these algorithms can also be applied to other Information Technology Applications such as Network Management, Data Mining and Signal Processing.

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Efficient Algorithms for Handling Molecular Weighted Sequences

Cited by 11 publications

References 15 publications

Weighted LCS

Weighted LCS

Algorithms for extracting motifs from biological weighted sequences

Motif Extraction from Biological Sequences: Trends and Contributions to Other Scientific Fields

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