Application of Needleman-Wunch Algorithm to identify mutation in DNA sequences of Corona virus

Irawan, Mohammad Isa; Mukhlash, Imam; Rizky, Abduh; Dewi, Alfiana Ririsati

doi:10.1088/1742-6596/1218/1/012031

Cited by 10 publications

(7 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…S1 ). These sequences were compared by global sequence alignment ( www.ebi.ac.uk/Tools/psa/ ) “for two sequences” using Needleman-Wunsch algorithm [ 29 ]. The two amino sequences of 201 residues of SGp RBD of Omicron and WT aligned for pairwise sequence alignment scoring gap penalty of 10.0 with similarity (180/201) and identity (175/201) is 89.6% and 87.1%, respectively ( Fig.…”

Section: Resultsmentioning

confidence: 99%

Computational studies on the interaction of SARS-CoV-2 Omicron SGp RBD with human receptor ACE2, limonin and glycyrrhizic acid

Vardhan

Sahoo

2022

Computers in Biology and Medicine

View full text Add to dashboard Cite

Section: Resultsmentioning

confidence: 99%

Computational studies on the interaction of SARS-CoV-2 Omicron SGp RBD with human receptor ACE2, limonin and glycyrrhizic acid

Vardhan

Sahoo

2022

Computers in Biology and Medicine

View full text Add to dashboard Cite

“…The alignment arrays for global alignment are analyzed. Table (6) lists the numbers of unique and repeated alignment arrays. Evidently, no repeated alignment arrays are found for global alignment because all the input DNA letters are fully represented in the alignment array.…”

Section: Proposed Algorithmmentioning

confidence: 99%

“…Consequently, producing timely results for large-scale problems requires more efficient algorithms and the use of parallel computing algorithms. PWSA can be used to identify the location of mutations between two viruses (e.g., coronaviruses), and this location can be used as a reference for the manufacture of coronavirus vaccines [6].…”

Section: Introductionmentioning

confidence: 99%

Sequence Alignment Using Machine Learning-Based Needleman–Wunsch Algorithm

et al. 2021

View full text Add to dashboard Cite

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.

show abstract

“…On the other hand, some researchers presented an effective model for determining mutation locations in the DNA sequences of this coronavirus [26]. Desautels et al [27] suggested a machine learning-based prediction model of antibody structures qualified for attacking the COVID-19 receptor-binding domain.…”

Section: Introductionmentioning

confidence: 99%

Computational predictions for protein sequences of COVID-19 virus via machine learning algorithms

Afify

Zanaty

2021

Med Biol Eng Comput

View full text Add to dashboard Cite

The rapid spread of coronavirus disease (COVID-19) has become a worldwide pandemic and affected more than 15 million patients reported in 27 countries. Therefore, the computational biology carrying this virus that correlates with the human population urgently needs to be understood. In this paper, the classification of the human protein sequences of COVID-19, according to the country, is presented based on machine learning algorithms. The proposed model is based on distinguishing 9238 sequences using three stages, including data preprocessing, data labeling, and classification. In the first stage, data preprocessing's function converts the amino acids of COVID-19 protein sequences into eight groups of numbers based on the amino acids' volume and dipole. It is based on the conjoint triad (CT) method. In the second stage, there are two methods for labeling data from 27 countries from 0 to 26. The first method is based on selecting one number for each country according to the code numbers of countries, while the second method is based on binary elements for each country. According to their countries, machine learning algorithms are used to discover different COVID-19 protein sequences in the last stage. The obtained results demonstrate 100% accuracy, 100% sensitivity, and 90% specificity via the country-based binary labeling method with a linear support vector machine (SVM) classifier. Furthermore, with significant infection data, the USA is more prone to correct classification compared to other countries with fewer data. The unbalanced data for COVID-19 protein sequences is considered a major issue, especially as the US's available data represents 76% of a total of 9238 sequences. The proposed model will act as a prediction tool for the COVID-19 protein sequences in different countries.

show abstract

Application of Needleman-Wunch Algorithm to identify mutation in DNA sequences of Corona virus

Cited by 10 publications

References 4 publications

Computational studies on the interaction of SARS-CoV-2 Omicron SGp RBD with human receptor ACE2, limonin and glycyrrhizic acid

Computational studies on the interaction of SARS-CoV-2 Omicron SGp RBD with human receptor ACE2, limonin and glycyrrhizic acid

Sequence Alignment Using Machine Learning-Based Needleman–Wunsch Algorithm

Computational predictions for protein sequences of COVID-19 virus via machine learning algorithms

Contact Info

Product

Resources

About