Adaptive trends of sequence compositional complexity over pandemic time in the SARS-CoV-2 coronavirus
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Abstract
During the spread of the COVID-19 pandemic, the SARS-CoV-2 coronavirus underwent mutation and recombination events that altered its genome compositional structure, thus providing an unprecedented opportunity to search for adaptive evolutionary trends in real-time. The mutation rate in coronavirus is known to be lower than expected for neutral evolution, thus suggesting a role for natural selection. We summarize the compositional heterogeneity of each viral genome by computing its Sequence Compositional Complexity (SCC). To study the full range of SCC diversity, random samples of high-quality coronavirus genomes covering pandemic time span were analyzed. We then search for evolutionary trends that could inform on the adaptive process of the virus to its human host by computing the phylogenetic ridge regression of SCC against time (i.e., the collection date of each viral isolate). In early samples, we find no statistical support for any trend in SCC, although the viral genome appears to evolve faster than Brownian Motion (BM) expectation. However, in samples taken after the emergence of high fitness variants, and despite the brief time span elapsed, a driven decreasing trend for SCC, and an increasing one for its absolute evolutionary rate, are detected, pointing to a role for selection in the evolution of SCC in coronavirus genomes. We conclude that the higher fitness of variant genomes leads to adaptive trends of SCC over pandemic time in the coronavirus.
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SciScore for 10.1101/2021.11.06.467547: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources A second approach uses a Python script (https://github.com/cris12gm/covid19/blob/master/getRandomSamples.py) to get random samples stratified by date. Pythonsuggested: (IPython, RRID:SCR_001658)Results from OddPub: Thank you for sharing your code.
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results …
SciScore for 10.1101/2021.11.06.467547: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources A second approach uses a Python script (https://github.com/cris12gm/covid19/blob/master/getRandomSamples.py) to get random samples stratified by date. Pythonsuggested: (IPython, RRID:SCR_001658)Results from OddPub: Thank you for sharing your code.
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
Results from scite Reference Check: We found no unreliable references.
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