Cluster Analysis of Coronavirus Sequences Using Computational Sequence Descriptors: with Applications to SARS, MERS and SARS-CoV-2 (Covid-19).
Current Computer - Aided Drug Design（2021）
Natl Inst Chem
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.
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Key words
SARS-CoV-2 (CoVID-19),SARS,MERS,mathematical representation of sequences,clustering,Euclidean distance,Mahalanobis distance,principal component analysis,alignment-free sequenc descriptors
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