The hCoV-19 virus is continuously evolving to highly infectious and lethal variants. There is a latent risk that current vaccines will not be effective over these novel variants. This entails comprehending the genome-wide viral information to unveil mutagenic mechanisms of hCoV-19. To date, this virus is studied as a collection of non-related variants, making it challenging to forecast hotspots and their upcoming effects. In this work, we explore genome-wide information to disentangle informational mechanisms that lead to insights into viral mutagenicity. Towards this aim, we modeled informational compartments based on a topic-free-alignment workflow. These compartments illustrate that hCoV-19 has a complex informational architecture that addresses high-level virus phenomena, i.e., mutagenicity. This new framework represents the first step towards identifying the virus mutagenicity leading to the development of all-variants-effective vaccines.