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Journal of Data Science


Statistical Challenges in the Analysis of Sequence and Structure Data for the COVID-19 Spike Protein
Volume 19, Issue 2 (2021): Special issue: Continued Data Science Contributions to COVID-19 Pandemic, pp. 314–333
Pub. online: 22 February 2021      Type: COVID-19 Special Issue      Open accessOpen Access
Received
31 December 2020
Accepted
18 January 2021
Published
22 February 2021

Abstract

As the major target of many vaccines and neutralizing antibodies against SARS-CoV-2, the spike (S) protein is observed to mutate over time. In this paper, we present statistical approaches to tackle some challenges associated with the analysis of S-protein data. We build a Bayesian hierarchical model to study the temporal and spatial evolution of S-protein sequences, after grouping the sequences into representative clusters. We then apply sampling methods to investigate possible changes to the S-protein’s 3-D structure as a result of commonly observed mutations. While the increasing spread of D614G variants has been noted in other research, our results also show that the co-occurring mutations of D614G together with S477N or A222V may spread even more rapidly, as quantified by our model estimates.

Supplementary material

 Supplementary Material
The processed data, R code, and instructions for reproducing the results in this paper are provided in a supplementary .zip file.

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Copyright
2021 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.
Open access article under the CC BY license.

Keywords
Bayesian hierarchical models compositional data analysis conformational sampling mutant clusters SARS-CoV-2

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