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Cluster Analysis of Coronavirus Sequences using Computational Sequence Descriptors: With Applications to SARS, MERS
Marjan Vračko1, Subhash C Basak2, Tathagata Dey3
1Theoretical Department. National Institute of Chemistry, Hajdrihova 19, 1000 Ljubljana, Slovenia.
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.
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