Omics Approaches and Technologies in Covid-19

The COVID-19 pandemic has affected the entire world in an unprecedented way since 2019. However, novel and innovative applications of various omics, computational, and smart technologies have helped manage the pandemic of the 21st century in a very effective manner. Omics approaches and technologies in COVID-19 presents up-to-date knowledge on omics, genetic engineering, mathematical and computational approaches, and advanced technologies in the diagnosis, prevention, monitoring, and management of COVID-19.

1. Omics approaches in COVID-19: An overview2. Genomics, metagenomics, and pan-genomics approaches in COVID-193. Genotype and phenotype correlations in COVID-194. Proteomic understanding of SARS-CoV-2 infection and COVID-19: Biological, diagnostic, and therapeutic perspectives5. Metabolites and metabolomics in COVID-196. Host-pathogen protein-protein interactions and interactomics in COVID-197. Currently available COVID-19 management options8. Transcriptomic approaches in COVID-19: From infection to vaccines9. miRNAomics in COVID-1910. Epigenetic features, methods, and implementations associated with COVID-1911. Nutrigenetics and nutrition aspects in COVID-1912. COVID-19 phenomics13. Applications of genetic engineering in COVID-1914. CRISPR-based assays for rapid detection of SARS-CoV-215. Emergence and dynamics of COVID-19 and future pandemics16. Artificial intelligence in COVID-1917. Applications of mathematical modeling and simulation in COVID-1918. In silico disease modeling for COVID-1919. Systems biology in COVID-1920. Computational approaches for drug discovery against COVID-1921. Computational approaches in COVID-19 vaccine development22. Applications of multiomics data in COVID-1923. Publicly available resources in COVID-19 research and their applications24. Emerging technologies for COVID-19, diagnosis, prevention, and management25. Applications of digital and smart technologies to control SARS-CoV-2 transmission, rapid diagnosis, and monitoring26. Application of big data analytics in the COVID-19 pandemic: Selected problems