KAUST researchers developed CovMT, a COVID-19 mutation tracking system for authorities and scientists to detect variants. CovMT tracks mutation fingerprints using daily data from the GISAID database of over 1.5 million viral genomes. The system identifies mutation hot spots, enabling public health authorities to stay ahead of new variants. Why it matters: This system provides a tool for rapid variant detection and informed public health decision-making in the region and globally.
KAUST researchers are analyzing the SARS-CoV-2 genome to identify potential targets for treatment and vaccine development. They are using the KAUST Metagenome Analysis Platform (KMAP) and the university's supercomputer to compare and analyze genomic data. The research focuses on identifying key genes for detection and treatment of COVID-19. Why it matters: This research contributes to the global effort to combat the pandemic and highlights KAUST's capabilities in genomic data analysis and computational bioscience.
The KAUST Pathogen Genomics Laboratory (PGL), led by Professor Arnab Pain, is using DNA and RNA sequencing to study the SARS-CoV-2 virus. The lab is part of KAUST's Rapid Research Response Team (R3T), supporting Saudi healthcare stakeholders in combating COVID-19. Pain and his Ph.D. student Sharif Hala are partnering with the Saudi-CDC and Ministry of Health hospitals to sequence Saudi SARS-CoV-2 samples. Why it matters: This effort provides crucial data for understanding and monitoring the virus's spread and evolution within the Kingdom, informing public health strategies.
A KAUST-led team developed NIRVANA, a portable, briefcase-sized device for rapid detection and sequencing of SARS-CoV-2, influenza, and other viruses. The test utilizes isothermal recombinase amplification (RPA) and was validated on clinical samples and wastewater. NIRVANA can differentiate SARS-CoV-2 strains and doesn't require expensive infrastructure. Why it matters: This innovation enables rapid, decentralized virus detection and surveillance, crucial for pandemic response and monitoring new variants across the region.
KAUST researchers developed a statistical approach to improve the identification of cancer-related protein mutations by reducing false positives. The method uses Bayesian statistics to analyze protein domain data from tumor samples, accounting for potential errors due to limited data. The team tested their method on prostate cancer data, successfully identifying a known cancer-linked mutation in the DNA binding protein cd00083. Why it matters: This enhances the reliability of cancer research at the molecular level, potentially accelerating the discovery of new therapeutic targets.