Comparison of Data Partitioning Schema of Parallel Pairwise Alignment on Shared Memory System

Akbar, Auriza Rahmad; Sukoco, Heru; Kusuma, Wisnu Ananta

doi:10.12928/telkomnika.v13i2.1415

Cited by 2 publications

(2 citation statements)

References 22 publications

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“…The ompInner scenario parallels directly on the pairwiseAlignment() function using the shared memory programming model. The risk of data dependency is addressed by synchronizing [7]. The synchronization is done in the form of checking the cells required in the calculation.…”

Section: Decomposition 321 Pairwise Alignmentmentioning

confidence: 99%

“…Implementation of parallel algorithms can be done partially to reduce overall computation time. This is done by [3] in parallelizing the first and third stages of ClustalW using MPI [5]- [6] only parallelize the neighbor-joining which is the second stage of ClustalW using the GPU [7] parallelize the pairwise alignment which is ClustalW's first stage using OpenMP.…”

Section: Introductionmentioning

confidence: 99%

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Parallelization of Pairwise Alignment and Neighbor-Joining Algorithm in Progressive Multiple Sequence Alignment

Utomo¹

2018

IJEECS

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Progressive multiple sequence alignment ClustalW is a widely used heuristic method for computing multiple sequence alignment (MSA). It has three stages: distance matrix computation using pairwise alignment, guide tree reconstruction using neighbor-joining and progressive alignment. To accelerate computing for large data, the progressive MSA algorithm needs to be parallelized. This research aims to identify, decompose and implement the pairwise alignment and neighbor-joining in progressive MSA using message passing, shared memory and hybrid programming model in the computer cluster. The experimental results obtained shared memory programming model as the best scenario implementation with speed up up to 12 times.

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Section: Decomposition 321 Pairwise Alignmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Parallelization of Pairwise Alignment and Neighbor-Joining Algorithm in Progressive Multiple Sequence Alignment

Utomo¹

2018

IJEECS

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Pairwise Sequence Alignment between HBV and HCC Using Modified Needleman Wunsch Algorithm

Muflikhah

Santoso

2017

TELKOMNIKA

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Ths paper aims to find similarity of Hepatitis B virus (HBV) and Hepatocelluler Carcinoma (HCC) DNA sequences. It is very important in bioformatics task. The similarity of sequence allignments indicates that they have similarity of chemical and physical properties. Mutation of the virus DNA in X region has potential role in HCC. It is observed using pairwise sequence alignment of genotype-A in HBV. The complexity of DNA sequence using dynamic programming, Needleman-Wunsch algorithm, is very high. Therefore, it is purpose to modifiy the method of Needleman Wunsch algorithm for optimum global DNA sequence alignment. The main idea is to optimize filling matrix and backtracking proccess of DNA components.This method can also solve various length of the both sequence alignment.This research is applied to DNA sequence of 858 hepatitis B virus and 12 carcinoma patient, so that there are 10,296 pairwis of sequences. They are aligned globally using the purposed method and as a result, it is achieved high similarity of 96.547% and validity of 99.854%. Furhthermore, this method has reduced the complexity of original Needleman-Wunsch algorithm The reduction of computational time is as 34.6% and space complexity is as 42.52%.

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Comparison of Data Partitioning Schema of Parallel Pairwise Alignment on Shared Memory System

Abstract: The pairwise alignment (PA)

Cited by 2 publications

References 22 publications

Parallelization of Pairwise Alignment and Neighbor-Joining Algorithm in Progressive Multiple Sequence Alignment

Parallelization of Pairwise Alignment and Neighbor-Joining Algorithm in Progressive Multiple Sequence Alignment

Pairwise Sequence Alignment between HBV and HCC Using Modified Needleman Wunsch Algorithm

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