Sequential and parallel algorithms for DNA sequencing

Błażewicz, Jacek; Kaczmarek, Janusz; Kasprzak, Marta; Markiewicz, Wojciech T.; Węglarz, Jan

doi:10.1093/bioinformatics/13.2.151

Cited by 12 publications

(5 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another source of difficulty exists when the spectrum contains repeated fragments. Most existing algorithms that allow for errors in the input spectrum put restrictions on the error model [5][10] [11]. There are few algorithms that do not have any restriction on the input error model.…”

Section: Preliminariesmentioning

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

View full text Add to dashboard Cite

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.

show abstract

Section: Preliminariesmentioning

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

View full text Add to dashboard Cite

show abstract

“…This can be done by combinatorial algorithms. There have been many successful solutions already presented in the literature (Lysov et al 1988;Dramanac et al 1989;Pevzner 1989;Bui and Youssef 2004;Blazewicz and Kasprzak 2003;Blazewicz et al 1997Blazewicz et al , 2002Blazewicz et al , 2004Blazewicz et al , 2006a. This paper presents a unique insight into the DNA sequencing by hybridization problem from the perspective of developing generic search techniques which could be applied to different combinatorial optimization problems.…”

Section: Introductionmentioning

confidence: 99%

Unified encoding for hyper-heuristics with application to bioinformatics

Świercz

Burke

Cichenski

et al. 2013

Cent Eur J Oper Res

Self Cite

View full text Add to dashboard Cite

This paper introduces a new approach to applying hyper-heuristic algorithms to solve combinatorial problems with less effort, taking into account the modelling and algorithm construction process. We propose a unified encoding of a solution and a set of low level heuristics which are domain-independent and which change the solution itself. This approach enables us to address NP-hard problems and generate good approximate solutions in a reasonable time without a large amount of additional work required to tailor search methodologies for the problem in hand. In particular, we focused on solving DNA sequencing by hybrydization with errors, which is known to be strongly NP-hard. The approach was extensively tested by solving multiple instances of well-known combinatorial problems and compared with results generated by meta heuristics that have been tailored for specific problem domains.

show abstract

“…The problem of reconstructing the sequence is NP-hard when there are hybridization errors [11]. However, several heuristics were proposed [4][5][6][7][8][9]13,15]. Halperin et al [12] gave an algorithm with provable performance in the following model: each k-tuple contained in the target appears in the (experimental) spectrum with probability 1 − q, and each k-tuple that is not contained in the target appears in the spectrum with probability p. In other words, the false negative probability is q, and the false positive probability is p. Furthermore, the appearance of a tuple is independent of the other k-tuples.…”

Section: Introductionmentioning

confidence: 99%

Sequencing by hybridization with errors: handling longer sequences

Tsur

2005

Theoretical Computer Science

View full text Add to dashboard Cite

Sequencing by hybridization (SBH) is a method for reconstructing a DNA sequence given the set of all subsequences of length k of the target sequence. This set, called the spectrum of the sequence, can be obtained from hybridization with a universal DNA chip. However, the hybridization experiments are error prone, so this leads to the computational problem of reconstructing a sequence from a noisy spectrum. Halperin et al. gave an algorithm for this problem with provable performance in the presence of both false positive and false negative errors. Assuming, for example, that the false positive rate is small, and the probability of false negative is 0.1, the algorithm can reconstruct a random sequence of length O(2 0.7k ) with an arbitrary small probability of failure. In this paper, we give an algorithm that can reconstruct longer sequences: under the assumptions above, our algorithm can reconstruct sequences of length O(2 0.942k ). This bound is almost optimal as the bound for the errorless case is (2 k ).

show abstract

Sequential and parallel algorithms for DNA sequencing

Cited by 12 publications

References 11 publications

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

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

Unified encoding for hyper-heuristics with application to bioinformatics

Sequencing by hybridization with errors: handling longer sequences

Contact Info

Product

Resources

About