Genome Motif Discovery in Zika Virus: Computational Techniques and Validation Using Greedy Method

Abstract

Identifying patterns in the genome sequences is an essential yet tricky task in Bioinformatics. It provides information about gene activity and gene functionality. In Deoxyribose Nucleic Acid (DNA) sequence analysis, computational approaches like Greedy motif search can be applied for motif identification. It finds recurring patterns called motifs by iteratively selecting the most promising sequence of a specified length from each DNA string. The selected string maximizes the scoring function and hence is selected. First, a set of initial motifs is selected for each set in the input string. Then, a subsequence that best aligns with the selected string is selected for the next iteration. The score is calculated and needs to be minimized. The validation of the obtained motif is also performed. This study focuses on applying the algorithm to identify patterns in the genome sequence of the Zika virus. In finding conserved patterns in the Zika virus genome sequence, the Greedy motif search is known for its efficiency and precision. The Greedy motif search results are compared with Gibbs sampler method of motif identification. This study adds knowledge of the viral genome and suggests new treatment development methods by confining these patterns.