The Experts below are selected from a list of 6600 Experts worldwide ranked by ideXlab platform
Muhamad Nasir Murad - One of the best experts on this subject based on the ideXlab platform.
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performance analysis of needleman wunsch Algorithm global and smith waterman Algorithm local in reducing search space and time for dna sequence alignment
Journal of Physics: Conference Series, 2018Co-Authors: F N Muhamad, R.b. Ahmad, Muhamad Nasir MuradAbstract:Generally, sequence alignment is the process of comparing two sequences to identify similarities and differences between them, then, a typical approach to solve this problem is to find a good and plausible alignment between the two sequences. The data representation in this work is DNA sequence. This work intends to analyze large sequences as well as reducing the search space and time complexity without compromising the accuracy and efficiency. This is by evaluating the performance of Needleman-Wunsch Algorithm (global) and Smith-Waterman Algorithm (local) based on the Dynamic Programming Algorithm. Dynamic Programming Algorithm is guaranteed to find optimal alignment by exploring all possible alignments and choosing the best through the scoring and traceback techniques, which is NP-hard to optimize. Implementation of parallel technique using OpenMP on Needleman-Wunsch Algorithm and Smith-Waterman Algorithm to identify the strengths and weaknesses for both Algorithms. By using C Programming, Needle and Smith programs are developed based on the Algorithms (respectively). The analysis concluded that the scoring and traceback techniques used in Needle and Smith are able to align an optimal alignment and improved the performance in searching similarity as well as reduced gaps and mismatch. OpenMP directives able to parallelize the codes and execute it faster, with four cores it can get an execution time of around 60% reduced.
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reducing the search space and time complexity of needleman wunsch Algorithm global alignment and smith waterman Algorithm local alignment for dna sequence alignment
Jurnal Teknologi, 2015Co-Authors: F N Muhamad, R.b. Ahmad, Muhamad Nasir MuradAbstract:The fundamental procedure of analyzing sequence content is sequence comparison. Sequence comparison can be defined as the problem of finding which parts of the sequences are similar and which parts are different, namely comparing two sequences to identify similarities and differences between them. A typical approach to solve this problem is to find a good and reasonable alignment between the two sequences. The main research in this project is to align the DNA sequences by using the Needleman-Wunsch Algorithm for global alignment and Smith-Waterman Algorithm for local alignment based on the Dynamic Programming Algorithm. The Dynamic Programming Algorithm is guaranteed to find optimal alignment by exploring all possible alignments and choosing the best through the scoring and traceback techniques. The Algorithms proposed and evaluated are to reduce the gaps in aligning sequences as well as the length of the sequences aligned without compromising the quality or correctness of results. In order to verify the accuracy and consistency of measurements obtained in Needleman-Wunsch and Smith-Waterman Algorithms the data is compared with Emboss (global) and Emboss (local) with 600 strands test data.
F N Muhamad - One of the best experts on this subject based on the ideXlab platform.
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performance analysis of needleman wunsch Algorithm global and smith waterman Algorithm local in reducing search space and time for dna sequence alignment
Journal of Physics: Conference Series, 2018Co-Authors: F N Muhamad, R.b. Ahmad, Muhamad Nasir MuradAbstract:Generally, sequence alignment is the process of comparing two sequences to identify similarities and differences between them, then, a typical approach to solve this problem is to find a good and plausible alignment between the two sequences. The data representation in this work is DNA sequence. This work intends to analyze large sequences as well as reducing the search space and time complexity without compromising the accuracy and efficiency. This is by evaluating the performance of Needleman-Wunsch Algorithm (global) and Smith-Waterman Algorithm (local) based on the Dynamic Programming Algorithm. Dynamic Programming Algorithm is guaranteed to find optimal alignment by exploring all possible alignments and choosing the best through the scoring and traceback techniques, which is NP-hard to optimize. Implementation of parallel technique using OpenMP on Needleman-Wunsch Algorithm and Smith-Waterman Algorithm to identify the strengths and weaknesses for both Algorithms. By using C Programming, Needle and Smith programs are developed based on the Algorithms (respectively). The analysis concluded that the scoring and traceback techniques used in Needle and Smith are able to align an optimal alignment and improved the performance in searching similarity as well as reduced gaps and mismatch. OpenMP directives able to parallelize the codes and execute it faster, with four cores it can get an execution time of around 60% reduced.
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reducing the search space and time complexity of needleman wunsch Algorithm global alignment and smith waterman Algorithm local alignment for dna sequence alignment
Jurnal Teknologi, 2015Co-Authors: F N Muhamad, R.b. Ahmad, Muhamad Nasir MuradAbstract:The fundamental procedure of analyzing sequence content is sequence comparison. Sequence comparison can be defined as the problem of finding which parts of the sequences are similar and which parts are different, namely comparing two sequences to identify similarities and differences between them. A typical approach to solve this problem is to find a good and reasonable alignment between the two sequences. The main research in this project is to align the DNA sequences by using the Needleman-Wunsch Algorithm for global alignment and Smith-Waterman Algorithm for local alignment based on the Dynamic Programming Algorithm. The Dynamic Programming Algorithm is guaranteed to find optimal alignment by exploring all possible alignments and choosing the best through the scoring and traceback techniques. The Algorithms proposed and evaluated are to reduce the gaps in aligning sequences as well as the length of the sequences aligned without compromising the quality or correctness of results. In order to verify the accuracy and consistency of measurements obtained in Needleman-Wunsch and Smith-Waterman Algorithms the data is compared with Emboss (global) and Emboss (local) with 600 strands test data.
Yongchao Liu - One of the best experts on this subject based on the ideXlab platform.
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CUDASW++2.0: enhanced Smith-Waterman protein database search on CUDA-enabled GPUs based on SIMT and virtualized SIMD abstractions
BMC Research Notes, 2010Co-Authors: Yongchao Liu, Bertil Schmidt, Douglas L MaskellAbstract:Background Due to its high sensitivity, the Smith-Waterman Algorithm is widely used for biological database searches. Unfortunately, the quadratic time complexity of this Algorithm makes it highly time-consuming. The exponential growth of biological databases further deteriorates the situation. To accelerate this Algorithm, many efforts have been made to develop techniques in high performance architectures, especially the recently emerging many-core architectures and their associated programming models. Findings This paper describes the latest release of the CUDASW++ software, CUDASW++ 2.0, which makes new contributions to Smith-Waterman protein database searches using compute unified device architecture (CUDA). A parallel Smith-Waterman Algorithm is proposed to further optimize the performance of CUDASW++ 1.0 based on the single instruction, multiple thread (SIMT) abstraction. For the first time, we have investigated a partitioned vectorized Smith-Waterman Algorithm using CUDA based on the virtualized single instruction, multiple data (SIMD) abstraction. The optimized SIMT and the partitioned vectorized Algorithms were benchmarked, and remarkably, have similar performance characteristics. CUDASW++ 2.0 achieves performance improvement over CUDASW++ 1.0 as much as 1.74 (1.72) times using the optimized SIMT Algorithm and up to 1.77 (1.66) times using the partitioned vectorized Algorithm, with a performance of up to 17 (30) billion cells update per second (GCUPS) on a single-GPU GeForce GTX 280 (dual-GPU GeForce GTX 295) graphics card. Conclusions CUDASW++ 2.0 is publicly available open-source software, written in CUDA and C++ programming languages. It obtains significant performance improvement over CUDASW++ 1.0 using either the optimized SIMT Algorithm or the partitioned vectorized Algorithm for Smith-Waterman protein database searches by fully exploiting the compute capability of commonly used CUDA-enabled low-cost GPUs.
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cudasw optimizing smith waterman sequence database searches for cuda enabled graphics processing units
BMC Research Notes, 2009Co-Authors: Yongchao Liu, Douglas L Maskell, Ertil SchmidAbstract:Background The Smith-Waterman Algorithm is one of the most widely used tools for searching biological sequence databases due to its high sensitivity. Unfortunately, the Smith-Waterman Algorithm is computationally demanding, which is further compounded by the exponential growth of sequence databases. The recent emergence of many-core architectures, and their associated programming interfaces, provides an opportunity to accelerate sequence database searches using commonly available and inexpensive hardware.
Douglas L Maskell - One of the best experts on this subject based on the ideXlab platform.
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CUDASW++2.0: enhanced Smith-Waterman protein database search on CUDA-enabled GPUs based on SIMT and virtualized SIMD abstractions
BMC Research Notes, 2010Co-Authors: Yongchao Liu, Bertil Schmidt, Douglas L MaskellAbstract:Background Due to its high sensitivity, the Smith-Waterman Algorithm is widely used for biological database searches. Unfortunately, the quadratic time complexity of this Algorithm makes it highly time-consuming. The exponential growth of biological databases further deteriorates the situation. To accelerate this Algorithm, many efforts have been made to develop techniques in high performance architectures, especially the recently emerging many-core architectures and their associated programming models. Findings This paper describes the latest release of the CUDASW++ software, CUDASW++ 2.0, which makes new contributions to Smith-Waterman protein database searches using compute unified device architecture (CUDA). A parallel Smith-Waterman Algorithm is proposed to further optimize the performance of CUDASW++ 1.0 based on the single instruction, multiple thread (SIMT) abstraction. For the first time, we have investigated a partitioned vectorized Smith-Waterman Algorithm using CUDA based on the virtualized single instruction, multiple data (SIMD) abstraction. The optimized SIMT and the partitioned vectorized Algorithms were benchmarked, and remarkably, have similar performance characteristics. CUDASW++ 2.0 achieves performance improvement over CUDASW++ 1.0 as much as 1.74 (1.72) times using the optimized SIMT Algorithm and up to 1.77 (1.66) times using the partitioned vectorized Algorithm, with a performance of up to 17 (30) billion cells update per second (GCUPS) on a single-GPU GeForce GTX 280 (dual-GPU GeForce GTX 295) graphics card. Conclusions CUDASW++ 2.0 is publicly available open-source software, written in CUDA and C++ programming languages. It obtains significant performance improvement over CUDASW++ 1.0 using either the optimized SIMT Algorithm or the partitioned vectorized Algorithm for Smith-Waterman protein database searches by fully exploiting the compute capability of commonly used CUDA-enabled low-cost GPUs.
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cudasw optimizing smith waterman sequence database searches for cuda enabled graphics processing units
BMC Research Notes, 2009Co-Authors: Yongchao Liu, Douglas L Maskell, Ertil SchmidAbstract:Background The Smith-Waterman Algorithm is one of the most widely used tools for searching biological sequence databases due to its high sensitivity. Unfortunately, the Smith-Waterman Algorithm is computationally demanding, which is further compounded by the exponential growth of sequence databases. The recent emergence of many-core architectures, and their associated programming interfaces, provides an opportunity to accelerate sequence database searches using commonly available and inexpensive hardware.
R.b. Ahmad - One of the best experts on this subject based on the ideXlab platform.
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performance analysis of needleman wunsch Algorithm global and smith waterman Algorithm local in reducing search space and time for dna sequence alignment
Journal of Physics: Conference Series, 2018Co-Authors: F N Muhamad, R.b. Ahmad, Muhamad Nasir MuradAbstract:Generally, sequence alignment is the process of comparing two sequences to identify similarities and differences between them, then, a typical approach to solve this problem is to find a good and plausible alignment between the two sequences. The data representation in this work is DNA sequence. This work intends to analyze large sequences as well as reducing the search space and time complexity without compromising the accuracy and efficiency. This is by evaluating the performance of Needleman-Wunsch Algorithm (global) and Smith-Waterman Algorithm (local) based on the Dynamic Programming Algorithm. Dynamic Programming Algorithm is guaranteed to find optimal alignment by exploring all possible alignments and choosing the best through the scoring and traceback techniques, which is NP-hard to optimize. Implementation of parallel technique using OpenMP on Needleman-Wunsch Algorithm and Smith-Waterman Algorithm to identify the strengths and weaknesses for both Algorithms. By using C Programming, Needle and Smith programs are developed based on the Algorithms (respectively). The analysis concluded that the scoring and traceback techniques used in Needle and Smith are able to align an optimal alignment and improved the performance in searching similarity as well as reduced gaps and mismatch. OpenMP directives able to parallelize the codes and execute it faster, with four cores it can get an execution time of around 60% reduced.
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reducing the search space and time complexity of needleman wunsch Algorithm global alignment and smith waterman Algorithm local alignment for dna sequence alignment
Jurnal Teknologi, 2015Co-Authors: F N Muhamad, R.b. Ahmad, Muhamad Nasir MuradAbstract:The fundamental procedure of analyzing sequence content is sequence comparison. Sequence comparison can be defined as the problem of finding which parts of the sequences are similar and which parts are different, namely comparing two sequences to identify similarities and differences between them. A typical approach to solve this problem is to find a good and reasonable alignment between the two sequences. The main research in this project is to align the DNA sequences by using the Needleman-Wunsch Algorithm for global alignment and Smith-Waterman Algorithm for local alignment based on the Dynamic Programming Algorithm. The Dynamic Programming Algorithm is guaranteed to find optimal alignment by exploring all possible alignments and choosing the best through the scoring and traceback techniques. The Algorithms proposed and evaluated are to reduce the gaps in aligning sequences as well as the length of the sequences aligned without compromising the quality or correctness of results. In order to verify the accuracy and consistency of measurements obtained in Needleman-Wunsch and Smith-Waterman Algorithms the data is compared with Emboss (global) and Emboss (local) with 600 strands test data.
