Abstract
The comparison of genomic sequences plays a key role in determining the structural and functional relationships between genes. This comparison is carried out by identifying the similarities, differences and mutations between genomic sequences. This makes it possible to study and analyze the genetic and the evolutionary relationships between organisms. Alignment algorithms have been in the spotlight for the last few decades, due to a vast genomic data explosion. They have attracted a great deal of interest from many researchers who focus on the development of practical solutions to ensure effective alignments with an optimal response time. In this paper, a novel algorithm based on Discrete To Continuous "DTC" approach has been developed. The proposed methodology was compared against other existing methods, which are largely based on the concept of string matching. Experimental results show that the DTC algorithm delivers supremely efficient alignment with a reduced response time.
Highlights
Bioinformatics is the intersection of biology and informatics because it is a field that covers life sciences disciplines such as genomics, proteomics and biology through computer methods
The sequence studied in this work is JN222368 for Genbank belonging to the marine sponge
The results of this study revealed that these methods have better average scanning speed performance than previous chain matching algorithms for deoxyribonucleic acid (DNA) sequences
Summary
Bioinformatics is the intersection of biology and informatics because it is a field that covers life sciences disciplines such as genomics, proteomics and biology through computer methods. The DNA sequence is an ordered collection of alphabets of the four nucleotides A, C, G and T containing the information necessary for the survival and reproduction of living beings. Analyzing this sequence is important and useful for both research on the life of organisms and for biomedical engineering. Dynamic programming relies on a relationship between the optimal solution of the problem and that of a finite number of subproblems. This means that it would be possible to deduce the optimal solution of a problem from an optimal solution of a sub-problem
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