Emphasis on minimalism prevails amidst the realm of Big Data
In a recent study, researchers focused on the Omicron XBB subvariant of SARS-CoV-2, a recombinant sub-variant that gained prominence in August 2022 for its high transmissibility. The author of the study, interested in positive/adaptive selection, delved into the evolution of the virus and the appearance of advantageous mutations in the viral population.
The researcher zeroed in on the Spike gene, as it codes for the Spike protein, which is crucial for Coronaviruses to enter host cells. After cleaning the Spike genes file, it was uploaded on FUBAR via the Datamonkey webserver for selection analysis. Approximate Spike sequences were aligned using Clustal on the EBI Webserver, and 414 quality sequences were selected for further analysis, based on completeness, high coverage, and patient status.
The study revealed that the Omicron XBB subvariant generally causes increased upper respiratory symptoms such as fever and sore throat compared to earlier Omicron subvariants like BA.1. However, the overall severity remains relatively low, with a small percentage of severe or critical cases. Specifically, later Omicron subvariants including XBB showed 24–38% higher odds of upper respiratory symptoms but did not markedly increase critical or fatal outcomes, with severe/critical cases typically ranging from 0% to 8% across studies and deaths remaining low (0–2%)[1].
Medical professionals can use these statistics to deduce the severity of Omicron XBB and implement health measures accordingly. From the generated pie chart, it is clear that 34% of patients from Omicron XBB are hospitalized, 3% die, 31% are released, and 30% are ambulatory or not hospitalized.
The study also employed Bayes factor for understanding positive selection, as it highlights the strongest peaks for strong positive selection sites. FUBAR's posterior probabilities greater than 0.9 indicate evidence of strong positive or strong negative selection. Jalview was used to remove redundant/duplicate sequences, ensuring a well-crafted data set for analysis.
Vaccination, especially with updated Omicron XBB.1.5-adapted mRNA vaccines, has been shown to be effective in reducing hospitalizations and severe outcomes related to this subvariant. Real-world data from the US and Europe demonstrate vaccine effectiveness ranging from approximately 40% to over 70% against severe COVID-19 outcomes for XBB.1.5 and closely related sublineages, particularly among older adults and high-risk groups[2][3].
In summary, while the XBB subvariant exhibits higher upper respiratory symptom frequency, especially fever and sore throat, it does not show substantially increased severity in terms of critical illness or mortality compared to earlier Omicron subvariants, particularly where vaccination coverage is high and updated vaccines targeting these subvariants are administered[1][2][3].
It's worth noting that while analyzing larger data sets can increase the accuracy of insights generated, it also becomes more time-consuming due to potential noise accumulation. Larger data sets can make it more difficult to find individual 'unknowns' or errors, which can disrupt smooth execution by algorithms in downstream processes. Therefore, analyzing smaller, well-crafted data sets is faster and allows for more time to extract useful information from the data.
As of the writing of this article, there were approximately 2,300 whole genome sequences of Omicron XBB uploaded on GISAID, providing a rich source of data for future studies. Omicron had become the predominant SARS-CoV-2 variant around this time last year, underscoring the importance of continued research and monitoring of new subvariants.
The author of the study utilized various tools in health-and-wellness technology, such as FUBAR, Datamonkey webserver, Clustal on the EBI Webserver, Jalview, and GISAID, to analyze the Omicron XBB subvariant, aiming to understand the evolution of the virus and its medical-conditions. With the increasing number of whole genome sequences of Omicron XBB on GISAID, there is a potential for future research in fitness-and-exercise, possibly studying the effects of the subvariant on physical health and recovery rates.
