The Evolving Faces of the SARS-CoV-2 Genome

Abstract

Surveillance of the evolving SARS-COV-2 genome combined with epidemiological monitoring and emerging vaccination became paramount tasks to control the pandemic which is rapidly changing in time and space. Genomic surveillance must combine generation and sharing sequence data with appropriate bioinformatics monitoring and analysis methods. We applied molecular portrayal using self-organizing maps machine learning (SOM portrayal) to characterize the diversity of the virus genomes, their mutual relatedness and development since the beginning of the pandemic. The genetic landscape obtained visualizes the relevant mutations in a lineage-specific fashion and provides developmental paths in genetic state space from early lineages towards the variants of concern alpha, beta, gamma and delta. The different genes of the virus have specific footprints in the landscape reflecting their biological impact. SOM portrayal provides a novel option for ‘bioinformatics surveillance’ of the pandemic, with strong odds regarding visualization, intuitive perception and ‘personalization’ of the mutational patterns of the virus genomes.

The Transcriptome and Methylome of the Developing and Aging Brain and Their Relations to Gliomas and Psychological Disorders

“…The extension SOM method (exSOM) [32] aims at adding secondary data to an already existing SOM without changing its metagene structure. We used exSOM to add expression and methylation data of lower-grade glioma (LGG) taken from [21] as secondary data to the respective expression and methylation SOM of the healthy developing brain.…”

“…We made use of gene signatures taken from a comparative study of autism (AUT), (a) The pairwise correlation heatmap between the samples indicates a strong correlation between different LGG-methylation and aging groups (red), e.g., between M1 (IDH-wt group) and the prenatal brain. The LGG methylation data were trained together with the healthy brain methylomes using exSOM transfer learning [32]. (b) The river plot visualizes the distribution of LGG tumor methylomes of the different M-groups among the age groups.…”

“…The LGG methylation data were trained together with the healthy brain methylomes using exSOM transfer learning [32]. The river plot visualizes distribution of LGG tumor methylomes of the different M-groups among the age groups.…”

Contrasting Epidemiology and Population Genetics of COVID-19 Infections Defined by Multilocus Genotypes in SARS-CoV-2 Genomes Sampled Globally

“…The GRY clade, which was absent in the "2020 genomes", showed clinal differentiation into the GR clade, with the former splitting from the later around September 2020 by acquiring deletions such as S (H69, V70 and Y144) and substitutions including S N501Y and N G204R [55]. VOCs including the Alpha, Beta, Gamma, and Delta variants, associated with the GRY, GH, GR, and G/GK clades, respectively, were responsible for large waves of SARS-CoV-2 transmission and COVID-19 cases in most parts of the world in 2021 [56,57]. Furthermore, the PCA also separated the MLGs in the "2021 genomes" based on the continent of origin (Figure 4B).…”

Mutational cascade of SARS-CoV-2 leading to evolution and emergence of omicron variant

“…Moreover, the real-time monitoring of evolutionary cascade of SARS-CoV-2 leading to novel variants is utmost. Earlier investigation of several VOC and VOI suggests some of the crucial mutations for viral survival and high infectivity in humans ( Boehm et al, 2021 ; Kumar & Bansal, 2021 ; Schmidt et al, 2021 ). However, mutations giving rise to omicron and intra-omicron genomic diversity are not yet analyzed at a population level.…”