Sequential algorithms for DNA sequencing

Błażewicz, Jacek; Kaczmarek, Janusz; Kasprzak, Marta; Węglarz, Jan

doi:10.12921/cmst.1996.01.01.31-42

Cited by 7 publications

(2 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…2. The speed-up has been computed as the quotient of the CPU time of the sequential version of the algorithm divided by the maximum CPU time used by the slave processes plus the master process CPU time [9,10].…”

Section: Simulation Resultsmentioning

confidence: 99%

The Efficiency of Transfer-matrix Simulations of Supramolecular Magnets in the Parallel Computing Environment

Matysiak¹,

Kamieniarz²

2006

CMST

View full text Add to dashboard Cite

The deterministic quantum transfer-matrix (QTM) technique and its mathematical background are presented. This important tool in computational physics can be applied to a class of the real physical low-dimensional magnetic systems described by the Heisenberg hamiltonian which includes the macroscopic molecular-based spin chains, small size magnetic clusters embedded in some supramolecules and other interesting compounds. In order to convert existing application for the susceptibility calculations to run on the grid, the speed-up and efficiency of parallelization are analyzed on the SGI Origin 3800 platform with p = 128 processor units. Using Message Parallel Interface (MPI) system libraries we find the efficiency of the code of 94% for p = 128 that makes our application suitable for the grid.

show abstract

Section: Simulation Resultsmentioning

confidence: 99%

The Efficiency of Transfer-matrix Simulations of Supramolecular Magnets in the Parallel Computing Environment

Matysiak¹,

Kamieniarz²

2006

CMST

View full text Add to dashboard Cite

show abstract

“…We do not require that the starting fragment of the sequence be known as it is done in [6]. We use a steady state genetic algorithm together with a local optimization procedure to help improve the performance of the algorithm.…”

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

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

Application of Parallel Computing in the Transfer — Matrix Simulations of the Supramolecules Mn6 and Ni12

Kamieniarz

Matysiak

D'Auria³

et al. 2002

Parallel Processing and Applied Mathematics

View full text Add to dashboard Cite

Sequential algorithms for DNA sequencing

Abstract: In the paper the problem of DNA sequencing is considered. The sequencing method by Pevzner [8] is analyzed and its generalization, allowing for finding all acceptable solutions, is described. It is then compared with another method based on a generation of a solution tree [4],

Cited by 7 publications

References 8 publications

The Efficiency of Transfer-matrix Simulations of Supramolecular Magnets in the Parallel Computing Environment

The Efficiency of Transfer-matrix Simulations of Supramolecular Magnets in the Parallel Computing Environment

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

Application of Parallel Computing in the Transfer — Matrix Simulations of the Supramolecules Mn6 and Ni12

Contact Info

Product

Resources

About