Research On Macro Clustering Visualization Of RNA Virus Genomic Sequences Based On Variant Measurement ——Integrated Entropy Analysis Model And Process Methods

The characteristics of different biological species in the world are different.The four bases in the genomic sequence are arranged without intervals to form the natural random code sequence.In the era of rapid development of computer technology,the traditional analysis and research methods of viral genome sequences have been unable to meet the needs of big data research on RNA virus genes with great variability.In the study of gene sequence analysis,information entropy is widely used for visual model analysis,but there are some limitations in comparison with classical methods.In this thesis,an analytical model and processing method of integrated entropy are proposed.Under measurements of mean entropy and integrated entropy,global invariants can be determined,which breaks through the spatial limitation of parameter space to form a uniquely distinguishable position on distribution diagram.A large number of genome sequences can be clustered in a specific geometric region.A total of tens of thousands genomes of SARS-COV-2 and other types of RNA virus sequences were selected.The genomic index maps show that distributions of genome sequences released by different countries and regions in the early days were different.SARS-COV-2 can form two characteristic patterns to provide measurement support for further discussion on the basis of tracing viruses.Shift operators to support the integration operation of the classical K-MERS algorithm involve the combinatorial operation of selected clusters.From a macro-statistical perspective,it adapts to different combination modes in a variety of situations.From the perspective of directional processing,this series of complex distributions has special experimental significance.The alignment of the RDRP regions of SARS-COV-2 using classical BLAST can form a strong clustering effect,which can provide the hierarchical structure requirements for optimizing the development of tree root nodes.The distance between the central mean locus and the entropy values of each sequence were calculated.From the measurement level,as the correlation standard of SARS-COV-2,the similarity conclusions obtained from classical biology were combined and compared to verify the effectiveness of the method in this thesis.Based on this general measure structure,it provides theoretical support for identifying the quantitative measurement range of SARS-COV-2 variation,to provide a new method for exploring source of viruses and homology analysis of functional modules.