Randomized Algorithm-Based Novel Approach to Detect Motif in the Genome of Zika Virus

Motif finding in deoxyribonucleic acid (DNA) has gained a lot of importance in bioinformatics. Motifs have complicated patterns. The motifs are used to identify the transcription factors and the associated binding sites. It is also used to find complicated regulatory patterns. It is interesting and challenging to find the motifs in DNA. The secrets of gene functions can be understood using motifs. Different motif models like (l, d) motif models are projected in literature. A motif is a biologically substantial entity. Motifs may undergo different variations in different classes of DNA. Motif finding remains a difficult task for researchers using a robust algorithm. Different categories of motif-finding algorithms exist, like numerical and probabilistic methods. DNA genome sequence of the Zika virus is collected and used to find motifs. Different techniques of motif finding can be used, and the score can be calculated. In this paper, we provide a randomized motif-finding algorithm for finding motifs in the genome of the Zika virus (ZIKV). The randomized motif-finding algorithms give promising results. The algorithms can be executed four to five times, and the lowest score value can be considered. The motif obtained with the lowest score can be considered the most promising result.