Dataset Information

Modeling SARS-CoV-2 nucleotide mutations as a stochastic process.

ABSTRACT: This study analyzes the SARS-CoV-2 genome sequence mutations by modeling its nucleotide mutations as a stochastic process in both the time-series and spatial domain of the gene sequence. In the time-series model, a Markov Chain embedded Poisson random process characterizes the mutation rate matrix, while the spatial gene sequence model delineates the distribution of mutation inter-occurrence distances. Our experiment focuses on five key variants of concern that had become a global concern due to their high transmissibility and virulence. The time-series results reveal distinct asymmetries in mutation rate and propensities among different nucleotides and across different strains, with a mean mutation rate of approximately 2 mutations per month. In particular, our spatial gene sequence results reveal some novel biological insights on the characteristic distribution of mutation inter-occurrence distances, which display a notable pattern similar to other natural diseases. Our findings contribute interesting insights to the underlying biological mechanism of SARS-CoV-2 mutations, bringing us one step closer to improving the accuracy of existing mutation prediction models. This research could also potentially pave the way for future work in adopting similar spatial random process models and advanced spatial pattern recognition algorithms in order to characterize mutations on other different kinds of virus families.

SUBMITTER: Lim Kai Rong M

PROVIDER: S-EPMC10146438 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Modeling SARS-CoV-2 nucleotide mutations as a stochastic process.

Lim Kai Rong Maverick M Kuruoglu Ercan Engin EE Chan Wai Kin Victor WKV

PloS one 20230428 4

This study analyzes the SARS-CoV-2 genome sequence mutations by modeling its nucleotide mutations as a stochastic process in both the time-series and spatial domain of the gene sequence. In the time-series model, a Markov Chain embedded Poisson random process characterizes the mutation rate matrix, while the spatial gene sequence model delineates the distribution of mutation inter-occurrence distances. Our experiment focuses on five key variants of concern that had become a global concern due to ...[more]

PMID: 37115784

Similar Datasets

Project description:The virus obtained from a swab sample ID: S66 in Hiroshima was reported to have a single T-base insertion in the ORF8 coding region. However, no T insertion was observed when we determined the genomic sequence using another method. We then extracted RNA from the S66 swab sample and sequenced the insertion site using the Sanger method. The resulting waveform was disrupted beyond the insertion site, suggesting the presence of a mixed population of viruses with different sequences. Through plasmid cloning of RT-PCR amplification fragments and virus cloning by limiting dilution, along with TIDE analysis to determine the ratio of components from the Sanger sequencing waveform, it was confirmed that the sample contained a mixture of viruses with varying numbers of T-base insertions. The virus with one T insertion (T1+) was predominant in 70-75 % of the genomes, and genomes with T0, T2+, T3+, T4+, and T5+ were also detected. No T-base insertion mutations were observed in the ORF8 region in three other SARS-CoV-2 samples. In the S66 sample, a C27911T point mutation near the insertion site in the ORF8 region resulted in a sequence of seven or more consecutive T bases, which was the cause of the T-base insertion. When the cloned S66 virus (T1+) was passaged in cultured cells, there was a tendency for viruses with more insertion bases to become dominant with successive generations, suggesting that the T-base insertion was due to polymerase stuttering. The insertion of T bases resulted in synthesis of deletion mutants of the ORF8 protein, but no significant change was observed in the proliferation of the viruses in cultured cells. A search of the GenBank database using NCBI BLAST for viruses similar to S66 with T-base insertion mutations revealed hundreds of viruses widely distributed on the molecular phylogenetic tree. These base insertion viruses were thought to have occasionally arisen during the virus infection process. This study suggests one mechanism of insertion mutations in SARS-CoV-2, and it is important to consider the emergence of future mutant strains.

Dataset Information

Modeling SARS-CoV-2 nucleotide mutations as a stochastic process.

Publications

Modeling SARS-CoV-2 nucleotide mutations as a stochastic process.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets