A Statistical Similarity/Dissimilarity Analysis of Protein Sequences Based on a Novel Group Representative Vector. (8th May 2019)

• Record Type: Journal Article
• Authors: Abd Elwahaab, Marwa A.; Abo-Elkhier, Mervat M.; Abo el Maaty, Moheb I.
• Other Names: Harrison Paul Academic Editor.
• Is Part Of: BioMed research international. Volume 2019(2019)
• Publication Date: 2019-05-08
• Subjects: Medicine -- Periodicals; Biology -- Periodicals; Biotechnology -- Periodicals; Life sciences -- Periodicals; 610.5
• DOI: 10.1155/2019/8702968
• Languages: English
• ISSNs: 2314-6133

Abstract

Abstract : Similarity/dissimilarity analysis is a key way of understanding the biology of an organism by knowing the origin of the new genes/sequences. Sequence data are grouped in terms of biological relationships. The number of sequences related to any group is susceptible to be increased every day. All the present alignment-free methods approve the utility of their approaches by producing a similarity/dissimilarity matrix. Although this matrix is clear, it measures the degree of similarity among sequences individually. In our work, a representative of each of three groups of protein sequences is introduced. A similarity/dissimilarity vector is evaluated instead of the ordinary similarity/dissimilarity matrix based on the group representative. The approach is applied on three selected groups of protein sequences: beta globin, NADH dehydrogenase subunit 5 (ND5), and spike protein sequences. A cross-grouping comparison is produced to ensure the singularity of each group. A qualitative comparison between our approach, previous articles, and the phylogenetic tree of these protein sequences proved the utility of our approach.