RISOTTO: Fast Extraction of Motifs with Mismatches

Pisanti, Nadia; Carvalho, Alexandra M.; Marsan, Laurent; Sagot, Marie‐France

doi:10.1007/11682462_69

Cited by 78 publications

(80 citation statements)

References 4 publications

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“…It works by constructing a generalized suffix tree using the input sequences and then finding the motifs (or spelling the models) using this generalized suffix tree. Following that many modifications and extensions were proposed to improve the performance [8] [9] [10]. In this paper we adapt the original SPELLER algorithm for efficient implementation on multicore and GPU.…”

Section: The Core Suffix Tree Algorithmmentioning

confidence: 99%

“…These algorithms are also referred to as exhaustive enumeration algorithms. SPELLER [4], MITRA [5], PMSprune [6], Voting [7], RISOTTO [8] are some approaches that fall under this category. These algorithms can further be classified into pattern-driven and sample driven approaches.…”

Section: Introductionmentioning

confidence: 99%

“…MITRA is more efficient than SPELLER in terms of both memory and space. SMILE [9], PSMILE [10], RISOTTO [8] are extensions to the SPELLER algorithm, RISOTTO being the most recent approach. It uses maximum extensibility to efficiently spell the motifs.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

High performance implementation of planted motif problem using suffix trees

Dasari

Ranjan

Zubair

2011

2011 International Conference on High Performance Computing &Amp; Simulation

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Section: The Core Suffix Tree Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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High performance implementation of planted motif problem using suffix trees

Dasari

Ranjan

Zubair

2011

2011 International Conference on High Performance Computing &Amp; Simulation

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“…A number of these algorithms find the approximate motif [1], [3], [4] and others find the exact motif [5], [6], [7], [8], [9], [10], [11], [12], [13], [14]. In this paper, we focus on the exact motif finding problem.…”

Section: Introductionmentioning

confidence: 99%

An efficient multicore implementation of planted motif problem

Dasari

Ranjan

Zubair

2010

2010 International Conference on High Performance Computing &Amp; Simulation

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“…The algorithm in [16], that we have described in Section 4.1, is actually the ancestor of a whole class of algorithms that use the suffix tree for the extraction of motifs from a sequence or a set of sequences ( [3,2,4,15,10,11,21,20,14]). …”

Section: Algorithms For Motif Discovery With Suffix Treementioning

confidence: 99%

Suffix Tree Characterization of Maximal Motifs in Biological Sequences

Federico

Pisanti

Communications in Computer and Information Science

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Finding motifs in biological sequences is one of the most intriguing problems for string algorithms designers due to, on the one hand, the numerous applications of this problem in molecular biology and, on the other hand, the challenging aspects of the computational problem. Indeed, when dealing with biological sequences it is necessary to work with approximations (that is, to identify fragments that are not necessarily identical, but just similar, according to a given similarity notion) and this complicates the problem. Existing algorithms run in time linear with respect to the input size. Nevertheless, the output size can be very large due to the approximation (namely exponential in the approximation degree). This often makes the output unreadable, next to slowing down the inference itself. A high degree of redundancy has been detected in the set of motifs that satisfy traditional requirements, even for exact motifs. Moreover, it has been observed many times that only a subset of these motifs, namely the maximal motifs, could be enough to provide the information of all of them. In this paper, we aim at removing such redundancy. We extend some notions of maximality already defined for exact motifs to the case of approximate motifs with Hamming distance, and we give a characterization of maximal motifs on the suffix tree. Given that this data structure is used by a whole class of motif extraction tools, we show how these tools can be modified to include the maximality requirement without changing the asymptotical complexity.

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RISOTTO: Fast Extraction of Motifs with Mismatches

Cited by 78 publications

References 4 publications

High performance implementation of planted motif problem using suffix trees

High performance implementation of planted motif problem using suffix trees

An efficient multicore implementation of planted motif problem

Suffix Tree Characterization of Maximal Motifs in Biological Sequences

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