Author: VraÄko, Marjan; Basak, Subhash C; Dey, Tathagata; Nandy, Ashesh
Title: Cluster analysis of coronavirus sequences using computational sequence descriptors: With applications to SARS, MERS and SARS-CoV-2 (CoVID-19). Cord-id: 3saxlyql Document date: 2021_2_1
ID: 3saxlyql
Snippet: BACKGROUND Study of 573 genome sequences belonging to SARS, MERS and SARS-CoV-2 (CoVID-19) viruses. OBJECTIVE To compare the virus sequences, which originate from different places around the world. METHODS Alignment free methods for representation of sequences and chemometrical methods for analyzing of clusters. RESULTS Majority of genome sequences are clustered with respect on virus type, but some of them are outliers. CONCLUSION We indicate 71 sequences, which tend to belong to more than clust
Document: BACKGROUND Study of 573 genome sequences belonging to SARS, MERS and SARS-CoV-2 (CoVID-19) viruses. OBJECTIVE To compare the virus sequences, which originate from different places around the world. METHODS Alignment free methods for representation of sequences and chemometrical methods for analyzing of clusters. RESULTS Majority of genome sequences are clustered with respect on virus type, but some of them are outliers. CONCLUSION We indicate 71 sequences, which tend to belong to more than cluster.
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