High-quality genome assembly of ancient asexual rotifer reveals homologous chromosomes that frequently recombine.

Monkeypox (MPOX), a zoonotic disease originating in Western and Central Africa in 1970, has seen a recent surge in outbreaks across 100+ countries. A comparative analysis of 404 Monkeypox virus (MPXV) genomes revealed notable changes in microsatellite abundance and density, especially within Clades I, IIa, and IIb. Each clade exhibited unique microsatellite motifs, with twenty-six conserved loci specific to MPXV, suggesting their potential as molecular markers in diagnostics. Additionally, nine genes in the MPXV genome featured ten variable hotspot microsatellite regions associated with surface protein synthesis and host control. Notably, gene OPG153, especially at the SSR locus ‘(ATC)n’, exhibited the most pronounced variations among lineages over time and plays a role in virus pathogenesis within the host cell. These findings not only enhance our understanding of MPXV unique molecular profile but also offer valuable insights into potential pathogenic and evolutionary implications.

Motivation: The Gene Ontology system facilitates the functional annotation of genes by categorizing them into specific biological processes, cellular components, and molecular functions. Despite numerous tools like DAVID and Enrichr, analysing non-model organisms remains challenging due to a lack of genetic information and available tools. Results: To address this, we present getENRICH, a comprehensive tool for gene enrichment analysis tailored for non-model organisms. Available in both command-line and web-based graphical user interface (GUI) formats, getENRICH facilitates user-friendly interaction for gene dataset uploads, parameter configuration, and visualization. getENRICH employs hypergeometric distribution for P-value calculation and Benjamini–Hochberg correction for multiple testing. Availability and implementation: getENRICH is freely available under the MIT license, with the source code, documentation, and example datasets available on GitHub (https://github.com/jnarayan81/getENRICH) and the GUI version available at https://getenrich.igib.res.in/.

The SARS-CoV-2 virus, a novel member of the Coronaviridae family, is responsible for the viral infection known as Coronavirus Disease 2019 (COVID-19). In response to the urgent and critical need for rapid detection, diagnosis, analysis, interpretation, and treatment of COVID-19, a wide variety of bioinformatics tools have been developed. Given the virulence of SARS-CoV-2, it is crucial to explore the pathophysiology of the virus. We intend to examine how bioinformatics, in conjunction with next-generation sequencing techniques, can be leveraged to improve current diagnostic tools and streamline vaccine development for emerging SARS-CoV-2 variants. We also emphasize how bioinformatics, in general, can contribute to critical areas of biomedicine, including clinical diagnostics, SARS-CoV-2 genomic surveillance and its evolution, identification of potential drug targets, and development of therapeutic strategies. Currently, state-of-the-art bioinformatics tools have helped overcome technical obstacles with respect to genomic surveillance and have assisted in rapid detection, diagnosis, and delivering precise treatment to individuals on time.

Background: COVID-19 pandemic had unprecedented global impact on health and society, highlighting the need for a detailed understanding of SARS-CoV-2 evolution in response to host and environmental factors. This study investigates the evolution of SARS-CoV-2 via mutation dynamics, focusing on distinct age cohorts, geographical location, and vaccination status within the Indian population, one of the nations most affected by COVID-19. Methodology: Comprehensive dataset, across diverse time points during the Alpha, Delta, and Omicron variant waves, captured essential phases of the pandemic’s footprint in India. By leveraging genomic data from Global Initiative on Sharing Avian Influenza Data (GISAID), we examined the substitution mutation landscape of SARS-CoV-2 in three demographic segments: children (1–17 years), working-age adults (18–64 years), and elderly individuals (65+ years). A balanced dataset of 69,975 samples was used for the study, comprising 23,325 samples from each group. This design ensured high statistical power, as confirmed by power analysis. We employed bioinformatics and statistical analyses, to explore genetic diversity patterns and substitution frequencies across the age groups. Principal findings: The working-age group exhibited a notably high frequency of unique substitutions, suggesting that immune pressures within highly interactive populations may accelerate viral adaptation. Geographic analysis emphasizes notable regional variation in substitution rates, potentially driven by population density and local transmission dynamics, while regions with more homogeneous strain circulation show relatively lower substitution rates. The analysis also revealed a significant surge in unique substitutions across all age groups during the vaccination period, with substitution rates remaining elevated even after widespread vaccination, compared to pre-vaccination levels. This trend supports the virus's adaptive response to heightened immune pressures from vaccination, as observed through the increased prevalence of substitutions in important regions of SARS-CoV-2 genome like ORF1ab and Spike, potentially contributing to immune escape and transmissibility. Conclusion: Our findings affirm the importance of continuous surveillance on viral evolution, particularly in countries with high transmission rates. This research provides insights for anticipating future viral outbreaks and refining pandemic preparedness strategies, thus enhancing our capacity for proactive global health responses.