Modeling SARS-CoV-2 nucleotide mutations as a stochastic process
Copyright: © 2023 Lim Kai Rong et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited..
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.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2023 |
|---|---|
| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:18 |
|---|---|
| Enthalten in: |
PloS one - 18(2023), 4 vom: 23., Seite e0284874 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Lim Kai Rong, Maverick [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
Journal Article |
|---|
| Anmerkungen: |
Date Completed 01.05.2023 Date Revised 11.05.2023 published: Electronic-eCollection Citation Status MEDLINE |
|---|
| doi: |
10.1371/journal.pone.0284874 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM356193462 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM356193462 | ||
| 003 | DE-627 | ||
| 005 | 20231226065937.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231226s2023 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1371/journal.pone.0284874 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1187.xml |
| 035 | |a (DE-627)NLM356193462 | ||
| 035 | |a (NLM)37115784 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Lim Kai Rong, Maverick |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Modeling SARS-CoV-2 nucleotide mutations as a stochastic process |
| 264 | 1 | |c 2023 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Completed 01.05.2023 | ||
| 500 | |a Date Revised 11.05.2023 | ||
| 500 | |a published: Electronic-eCollection | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright: © 2023 Lim Kai Rong et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 7 | |a Nucleotides |2 NLM | |
| 650 | 7 | |a Spike Glycoprotein, Coronavirus |2 NLM | |
| 650 | 7 | |a spike protein, SARS-CoV-2 |2 NLM | |
| 700 | 1 | |a Kuruoglu, Ercan Engin |e verfasserin |4 aut | |
| 700 | 1 | |a Chan, Wai Kin Victor |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t PloS one |d 2006 |g 18(2023), 4 vom: 23., Seite e0284874 |w (DE-627)NLM167327399 |x 1932-6203 |7 nnns |
| 773 | 1 | 8 | |g volume:18 |g year:2023 |g number:4 |g day:23 |g pages:e0284874 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1371/journal.pone.0284874 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 18 |j 2023 |e 4 |b 23 |h e0284874 | ||