SLPal: Accelerating Long Sequence Alignment on Many-Core and Multi-Core Architectures

Xu, Xiaoming; Chan, Yuandong; Xu, Kai; Zhang, Jikai; Wang, Xiaoning; Yin, Zekun; Liu, Weiguo

doi:10.1109/bibm49941.2020.9313429

Cited by 6 publications

(5 citation statements)

References 19 publications

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“…However, these techniques have a lengthy calculation time [18]. It is divided into two stages [19]: 1-Stage 1 Given input sequences x and y of lengths K and L, respectively, calculate the similarity score. In order to implement the Needleman-Wunsch method, a dynamic programming (DP) matrix F is kept up-to-date.…”

Section: Needleman-wunsch Algorithmmentioning

confidence: 99%

Developing New Pairwise Sequence Alignment Method Based on NeedlemanWunsch Algorithm

2023

IJIES

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Molecular biology and bioinformatics are fields that alter knowledge and abilities for gathering, managing, storing, analyzing, interpreting, and disseminating biological data. In order to get insight into the design and testing of life sciences, it is necessary to make use of high-performance computers, cutting-edge software tools, and unique algorithmic approaches for data analysis, interpretation, and prognostication. The computing challenges of processing biological sequences are used in this research to describe a significant technique for producing DNA sequence alignments. One of these challenges is to put forward computational models that aid in speeding up the computation of the substitution matrix of the needleman-wunsch algorithm. Another difficulty is the implementation of a parallel program for two threads to find the values of the matrix concurrently in order to achieve high efficiency and find the best sequence alignment in less time. This paper introduces a novel approach to constructing the substitution matrix of the Needleman-Winch algorithm. This approach enhances the overall performance of the algorithm by adjusting the values of the penalties imposed for the gap, mismatch, and match in addition to computing the matrix using two parallel threads depending on the progress made in the devices, especially with regard to the capabilities and performance of the GPU. Numerous tests have been run using different data sets and different lengths of sequence. A comparison was made between the amount of time needed to implement the Needleman method and the amount of time needed to implement the suggested model using the same data in order to evaluate the performance of the proposed model. Processing time and speedup for parallel performance are experimentally calculated. The recommended model has high scalability in terms of workload and machine capacity, according to the performance evaluation and scalability assessments. The proposed approach reduced the execution time ratio by 47% to 90%, and this improvement rate rises as the length of the reference and query sequences does.

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Section: Needleman-wunsch Algorithmmentioning

confidence: 99%

Developing New Pairwise Sequence Alignment Method Based on NeedlemanWunsch Algorithm

2023

IJIES

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“…There are four phases in the implementation: matrix initialization, similarity score calculation, traceback and result generation [ 9 ]. As shown in Fig.…”

Section: Implementation Details: the Step-by-step Visualization Proce...mentioning

confidence: 99%

A Python-based educational software tool for visualizing bioinformatics alignment algorithms

Khatizah¹,

Nam²,

Park³

2023

Genomics Inform

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Bioinformatics education can be defined as the teaching and learning of how to use software tools, along with mathematical and statistical analysis, to solve biological problems. Although many resources are available, most students still struggle to understand even the simplest sequence alignment algorithms. Applying visualizations to these topics benefits both lecturers and students. Unfortunately, educational software for visualizing step-by-step processes in the user experience of sequence alignment algorithms is rare. In this article, an educational visualization tool for biological sequence alignment is presented, and the source code is released in order to encourage the collaborative power of open-source software, with the expectation of further contributions from the community in the future. Two different modules are integrated to enable a student to investigate the characteristics of alignment algorithms.

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“…The NW algorithm is a type of pairwise sequence alignment algorithm based on DP; it is mainly used to obtain the optimal global alignment between two biological sequences (e.g., DNA, RNA, and protein) in O(MN) time and space. The NW algorithm is divided into four phases: matrix initialization, similarity score calculation, traceback, and result generation [8]. Figure 1 shows the pseudocode of the algorithm for computing the scoring H matrix.…”

Section: A Nw Algorithmmentioning

confidence: 99%

Sequence Alignment Using Machine Learning-Based Needleman–Wunsch Algorithm

et al. 2021

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Biological pairwise sequence alignment can be used as a method for arranging two biological sequence characters to identify regions of similarity. This operation has elicited considerable interest due to its significant influence on various critical aspects of life (e.g., identifying mutations in coronaviruses). Sequence alignment over large databases cannot yield results within a reasonable time, power, and cost. heuristic methods, such as FASTA, the BLAST family have been demonstrated to perform 40 times faster than DP-based (e.g., Needleman-Wunsch) techniques they cannot guarantee an optimum alignment result An optimized software platform of a widely used DNA sequence alignment algorithm called the Needleman-Wunsch (NW) algorithm based on a lookup table, is described in this study. This global alignment algorithm is the best approach for identifying similar regions between sequences. This study presents a new application of classical machine learning (ML) to global sequence alignment. Customized ML models are used to implement NW global alignment. An accuracy of 99.7% is achieved when using a multilayer perceptron with the ADAM optimizer, and up to 2912 Giga cell updates per second are realized on two real DNA sequences with a length of 4.1 M nucleotides. Our implementation is valid for RNA/DNA sequences. This study aims to parallelize the computation steps involved in the algorithm to accelerate its performance by using ML algorithms. All datasets used in this study are available from https://ieeedataport.org/documents/dna-sequence-alignment-datasets-based-nw-algorithm. INDEX TERMSBioinformatics, DNA, RNA, Pairwise sequence alignment (PWSA), Needleman-Wunsch (NW) algorithm, Machine learning (ML) algorithms, Multilayer perceptron (MLP), XGBoost algorithm. CONTRIBUTION:This study presented six DNA/RNA sequence alignment datasets for one of the most common alignment algorithms, namely, the Needleman-Wunsch (NW) algorithm. It proposed a fast and parallel implementation of the NW algorithm by using machine learning techniques. This research is an extension and improved version of our previous work [1]. The current implementation achieved 99.7% accuracy by using a multilayer perceptron with the ADAM optimizer and up to 2912 Giga cell updates per second on two real DNA sequences with an of length 4.1 M nucleotides. Our implementation is valid for extremely long sequences by using the divide-and-conquer strategy.

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SLPal: Accelerating Long Sequence Alignment on Many-Core and Multi-Core Architectures

Cited by 6 publications

References 19 publications

Developing New Pairwise Sequence Alignment Method Based on NeedlemanWunsch Algorithm

Developing New Pairwise Sequence Alignment Method Based on NeedlemanWunsch Algorithm

A Python-based educational software tool for visualizing bioinformatics alignment algorithms

Sequence Alignment Using Machine Learning-Based Needleman–Wunsch Algorithm

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