Serial and parallel implementation of Needleman-Wunsch algorithm

Lee, Yun Sup; Kim, Yu Sin; Uy, Roger Luis

doi:10.26555/ijain.v6i1.361

Cited by 6 publications

(6 citation statements)

References 25 publications

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“…Needleman-Wunsch(NW) algorithm is a prominent dynamic programming-based method used for the global alignment of DNA sequences [24]. For any two given DNA sequences, many possible alignments exist between them.…”

Section: A Needleman-wunsch(nw) Methodsmentioning

confidence: 99%

Performance Analysis of Parallel Implementations of Global Sequence Alignment Methods for DNA Sequencing

Yadav¹,

Ahmed²,

Khan³

2023

IJCDS

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Global sequence alignment is an essential process in bio-informatics for determining the degree of similarity between two DNA sequences. It is performed using the Dynamic Programming (DP) approach, in which the problem is broken down into smaller ones, and these sub-problems are solved individually. There are various dynamic programming-based approaches to performing global sequence alignments, e.g.,Needleman-Wunsch (NW), Longest Common Subsequences (LCS). It is computationally difficult and complex to perform a global sequence alignment operation when the given DNA sequences are very large because programs based on dynamic programming are generally written to run sequentially. On the other hand, A well-written parallelized code executes many times faster than its equivalent serial code. This paper first explains the NW and LCS methods. Then the parallelization of these methods for shared memory and distributed memory architecture using OpenMP and MPI parallel paradigms is discussed in detail and compares the sequential and parallelized version of the Needleman-Wunsch and longest common subsequences. The detailed experiments for sequential and parallelized implementation of both methods were carried out by varying the DNA sequence length and number of threads in the case of OpenMP-based parallelization and the number of processes in the case of MPI-based parallelization. The obtained experimental results helped to perform a comparative performance evaluation based on the execution time of the sequential and parallel implementation of the methods. The speedup analysis of results showed that the parallelized implementations are many times faster than the equivalent sequential implementation, and MPI-based parallel implementation is approximately three times faster than the OpenMP-based parallel implementation.

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Section: A Needleman-wunsch(nw) Methodsmentioning

confidence: 99%

Performance Analysis of Parallel Implementations of Global Sequence Alignment Methods for DNA Sequencing

Yadav¹,

Ahmed²,

Khan³

2023

IJCDS

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“…And consequently, two documents are similar if they have similar strings. Some approaches deal with the text as a sequence of characters (String), such as Longest Common Subsequence [25], Jaro [26], Damerau -Levenshtein [27] and Needleman -Wunsch [28]. And other approaches consider the text as words governed by syntaxes such as Block Distance, Cosine similarity [29], Dice's coefficient [30], Euclidean distance (L2), and Jaccard similarity [31].…”

Section: Related Workmentioning

confidence: 99%

Similarity Detection of Time-Sensitive Online News Articles Based on RSS Feeds and Contextual Data

Daoud¹

2023

AETiC

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This article tackles the problem of finding similarity between web time-sensitive news articles, which can be a challenge. This challenge was approached with a novel methodology that uses supervised learning algorithms with carefully selected features (Semantic, Lexical and Temporal features (content and contextual features)). The proposed approach considers not only the textual content, which is a well-studied approach that may yield misleading results, but also the context, community engagement, and community-deduced importance of that news article. This paper details the major procedures of title pair pre-processing, analysis of lexical units, feature engineering, and similarity measures. Thousands of web articles are being published every second, and therefore, it is essential to determine the similarity of these articles efficiently without wasting time on unnecessary text processing of the bodies. Hence, the proposed approach focuses on short contents (titles) and context. The conducted experiment showed high precision and accuracy on a Really Simple Syndication (RSS) dataset of 8000 Arabic news article pairs collected automatically from 10 different news sources. The proposed approach achieved an accuracy of 0.81. Contextual features increased the accuracy and the precision. The proposed algorithm achieved a 0.89 correlation with the evaluations of two human judges based on Pearson’s Correlation Coefficient. The results outperform the state-of-the-art systems on Arabic news articles.

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“…In [20], the researchers aimed to parallelize the computation involved in the SW algorithm to accelerate performance by using CUDA. They introduced a heterogeneous anti-diagonal approach that benefits from the interaction between the CPU-based serial design and the GPU-based parallel design.…”

Section: Related Workmentioning

confidence: 99%

“…Table (19) presents the summary of the results on the test datasets after using our proposed technique to prevent overfitting [36]. This strategy is summarized in Table (20).…”

Section: F Overfitting Preventionmentioning

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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Serial and parallel implementation of Needleman-Wunsch algorithm

Cited by 6 publications

References 25 publications

Performance Analysis of Parallel Implementations of Global Sequence Alignment Methods for DNA Sequencing

Performance Analysis of Parallel Implementations of Global Sequence Alignment Methods for DNA Sequencing

Similarity Detection of Time-Sensitive Online News Articles Based on RSS Feeds and Contextual Data

Sequence Alignment Using Machine Learning-Based Needleman–Wunsch Algorithm

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