ABSTRACT
The large amount and diversity of viral genomic datasets generated by next-generation sequencing technologies poses a set of challenges for computational data analysis workflows, including rigorous quality control, adaptation to higher sample coverage, and tailored steps for specific applications. Here, we present V-pipe 3.0, a computational pipeline designed for analyzing next-generation sequencing data of short viral genomes. It is developed to enable reproducible, scalable, adaptable, and transparent inference of genetic diversity of viral samples. By presenting two large-scale data analysis projects, we demonstrate the effectiveness of V-pipe 3.0 in supporting sustainable viral genomic data science.
ABSTRACT
Background During the SARS-CoV-2 pandemic, many countries directed substantial resources towards genomic surveillance to detect and track viral variants. There is a debate over how much sequencing effort is necessary in national surveillance programs for SARS-CoV-2 and future pandemic threats. Aim We aimed to investigate the effect of reduced sequencing on surveillance outcomes in a large genomic dataset from Switzerland, comprising more than 143k sequences. Methods We employed a uniform downsampling strategy using 100 iterations each to investigate the effects of fewer available sequences on the surveillance outcomes: (i) first detection of variants of concern (VOCs), (ii) speed of introduction of VOCs, (iii) diversity of lineages, (iv) first cluster detection of VOCs, (v) density of active clusters, and (vi) geographic spread of clusters. Results The impact of downsampling on VOC detection is disparate for the three VOC lineages , but many outcomes including introduction and cluster detection could be recapitulated even with only 35% of the original sequencing effort. The effect on the observed speed of introduction and first detection of clusters was more sensitive to reduced sequencing effort for some VOCs, in particular Omicron and Delta, respectively. Conclusion A genomic surveillance program needs a balance between societal benefits and costs. While the overall national dynamics of the pandemic could be recapitulated by a reduced sequencing effort, the effect is strongly lineage dependent - something that is unknown at the time of sequencing - and comes at the cost of accuracy, in particular for tracking the emergence of potential VOCs.
ABSTRACT
ObjectiveThe excess of visceral adipose tissue might hinder and delay the immune response. How people with abdominal obesity will respond to mRNA vaccines against SARS-CoV-2 is yet to be established. We evaluated SARS-CoV-2-specific antibody responses after the first and second dose of the BNT162b2 mRNA vaccine comparing the response of individuals affected by abdominal obesity (AO) to those without, discerning between individuals with or without prior infection. MethodsIgG neutralizing antibodies against the Trimeric complex (IgG-TrimericS) were measured at four time points: at baseline, at day 21 after vaccine dose-1, at one month and three months after dose-2. Nucleocapsid antibodies were assessed to detect prior SARS-CoV-2 infection. Waist circumference was measured to determine abdominal obesity. ResultsBetween the first and third month after vaccine dose-2, the drop in IgG-TrimericS levels was more remarkable in individuals with AO compared to those without AO (2.44 fold [95%CI: 2.22-2.63] vs 1.82 fold [95%CI: 1.69-1.92], respectively, p<0.001). Multiple linear regression confirmed this result even when adjusting for possible confounders (p<0.001). ConclusionsOur findings highlight the need to extend the duration of serological monitoring of antibody levels in infection-naive individuals with abdominal obesity, a higher-risk population category in terms of possible weaker antibody response.