DNA Sequencing With Positive and Negative Errors

Błażewicz, Jacek; Formanowicz, Piotr; Kasprzak, Marta; Markiewicz, Wojciech T.; Węglarz, Jan

doi:10.1089/cmb.1999.6.113

Cited by 57 publications

(30 citation statements)

References 16 publications

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“…The greedy algorithm presented in this paper is a heuristic based on the greedy algorithm defined in [9] for the SBH problem with positive and negative errors. The original algorithm has been extended to take into consideration the information about l-mers multiplicity.…”

Section: Algorithmmentioning

confidence: 99%

A greedy algorithm for the DNA sequencing by hybridization with positive and negative errors and information about repetitions

Kwarciak¹,

Formanowicz²

2011

Bulletin of the Polish Academy of Sciences: Technical Sciences

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Abstract. In this paper a greedy algorithm for some variants of the sequencing by hybridization method is presented. In the standard version of the method information about repetitions is not available. In the paper it is assumed that a partial information of this type is a part of the problem instance. Here two simple but realistic models of this information are taken into consideration. The first one assumes it is known if a given element of a spectrum appears in the target sequence once or more than once. The second model uses the knowledge if a given element of a spectrum occurs in the analyzed sequence once, twice or at least three times. The proposed greedy algorithm solves the variant of the problem with positive and negative errors. Results of a computational experiment are reported which, among others, confirm that the additional information leads to the improvement of the obtained solutions. They also show that the more precise model of information increases the quality of reconstructed sequences.

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Section: Algorithmmentioning

confidence: 99%

A greedy algorithm for the DNA sequencing by hybridization with positive and negative errors and information about repetitions

Kwarciak¹,

Formanowicz²

2011

Bulletin of the Polish Academy of Sciences: Technical Sciences

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“…In this work we consider the optimization problem that was introduced as a model for DNA sequencing by hybridization by B lażewicz et al in [3]. In [7] it was shown that this model is N P -hard.…”

Section: Dna Sequencing By Hybridizationmentioning

confidence: 99%

“…The first approach to solve the SBH problem was a branch & bound method proposed in [3]. However, this approach becomes unpractical with growing problem size.…”

Section: Existing Approachesmentioning

confidence: 99%

“…Both approaches were proposed by B lażewicz and colleagues; the first one is a look-ahead greedy (LAG) technique that was proposed in [3], and the second one called OW was proposed in [2] (see Table 1). Our motivation is to develop new types of constructive heuristics that can possibly lead to the development of better metaheuristic approaches.…”

Section: Motivation and Organization Of The Papermentioning

confidence: 99%

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New Constructive Heuristics for DNA Sequencing by Hybridization

Blum

Vallès

2006

Lecture Notes in Computer Science

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Deoxyribonucleic acid (DNA) is a molecule that consists of two complementary sequences of amino acids. Reading these sequences is an important task in biology, called DNA sequencing. However, large DNA molecules cannot be read in one piece. Therefore, existing techniques first break the given DNA molecules up into small fragments which can be read. One of these techniques is called the hybridization experiment. The reconstruction of the original DNA molecule from these fragments is a challenging problem from the computational point of view. In recent years the specific problem of DNA sequencing by hybridization has attracted quite a lot of interest in the optimization community. While most researchers focused on the development of metaheuristic approaches, work on simple constructive heuristics hardly received any attention. This is despite the fact that well-working constructive heuristics are often an essential component of succesful metaheuristics. It is exactly this lack of constructive heuristics that motivated the work presented in this paper. The results of our best constructive heuristic are comparable to the results of the best existing metaheuristics, while using less computational time.

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“…. m] of fragment indexes, the corresponding sequence is obtained by merging the fragments F u [1] , F u [2] , . .…”

Section: Algorithmmentioning

confidence: 99%

An Enhanced Genetic Algorithm for DNA Sequencing by Hybridization with Positive and Negative Errors

Bui

Youssef

2004

Genetic and Evolutionary Computation – GECCO 2004

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Abstract. This paper describes a genetic algorithm for the DNA sequencing problem. The algorithm allows the input spectrum to contain both positive and negative errors as could be expected from a hybridization experiment. The main features of the algorithm include a preprocessing step that reduces the size of the input spectrum and an efficient local optimization. In experimental tests, the algorithm performed very well against existing algorithms. The algorithm also performed very well on a large data set generated in this paper from real genomes data.

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DNA Sequencing With Positive and Negative Errors

Cited by 57 publications

References 16 publications

A greedy algorithm for the DNA sequencing by hybridization with positive and negative errors and information about repetitions

A greedy algorithm for the DNA sequencing by hybridization with positive and negative errors and information about repetitions

New Constructive Heuristics for DNA Sequencing by Hybridization

An Enhanced Genetic Algorithm for DNA Sequencing by Hybridization with Positive and Negative Errors

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