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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   Pages 1 - 10 ( 10 )

Abstract:


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.

Objective: 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.

Affiliation:

Theoretical Department. National Institute of Chemistry, Hajdrihova 19, 1000 Ljubljana, Theoretical Department. National Institute of Chemistry, Hajdrihova 19, 1000 Ljubljana, Centre for Interdisciplinary Research and Education, Kolkata, Centre for Interdisciplinary Research and Education, Kolkata



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