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Current Computer-Aided Drug Design

Editor-in-Chief

ISSN (Print): 1573-4099
ISSN (Online): 1875-6697

General Research Article

Cluster Analysis of Coronavirus Sequences using Computational Sequence Descriptors: With Applications to SARS, MERS and SARS-CoV-2 (CoVID-19)

Author(s): Marjan Vračko, Subhash C. Basak*, Tathagata Dey and Ashesh Nandy

Volume 17 , Issue 7 , 2021

Published on: 02 February, 2021

Page: [936 - 945] Pages: 10

DOI: 10.2174/1573409917666210202092646

Abstract

Introduction: Coronaviruses comprise a group of enveloped, positive-sense single-stranded RNA viruses that infect humans as well as a wide range of animals. The study was performed on a set of 573 sequences belonging to SARS, MERS and SARS-CoV-2 (CoVID-19) viruses. The sequences were represented with alignment-free sequence descriptors and analyzed with different chemometric methods: Euclidean/Mahalanobis distances, principal component analysis and self-organizing maps (Kohonen networks). We report the cluster structures of the data. The sequences are well-clustered regarding the type of virus; however, some of them show the tendency to belong to more than one virus type.

Background: This is a study of 573 genome sequences belonging to SARS, MERS and SARS-- CoV-2 (CoVID-19) coronaviruses.

Objectives: The aim was to compare the virus sequences, which originate from different places around the world.

Methods: The study used alignment free sequence descriptors for the representation of sequences and chemometric methods for analyzing clusters.

Results: Majority of genome sequences are clustered with respect to the virus type, but some of them are outliers.

Conclusion: We indicate 71 sequences, which tend to belong to more than one cluster.

Keywords: SARS-CoV-2 (CoVID-19), SARS, MERS, mathematical representation of sequences, clustering, Euclidean distance, Mahalanobis distance, principal component analysis, alignment-free sequenc descriptors.

Graphical Abstract

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