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 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.
KAUST and KFSHRC have developed NanoRanger, a new gene sequencing system for identifying mutations causing genetic diseases. NanoRanger offers a faster and simpler process to detect DNA abnormalities at base resolution, building on existing long-read sequencing technologies. The system is designed to be cheaper and faster, targeting diseases prevalent in Saudi Arabia due to consanguinity. Why it matters: The technology has the potential to improve diagnosis and treatment of Mendelian diseases, which are especially prevalent in the Arab world.
Petar Stojanov from the Broad Institute of MIT and Harvard will give a talk on cancer data analysis, covering the fundamentals of cancer, the nature of large-scale data collected, and main analysis objectives. The talk will also address open questions in cancer data analysis and how machine learning and generative modeling can help. Stojanov's research focuses on applying machine learning to genomic analysis of cancer mutation and single-cell RNA sequencing data. Why it matters: Applying AI and machine learning to cancer research can lead to a better understanding of the disease and development of new therapies.
KAUST and King Faisal Specialist Hospital and Research Centre (KFSHRC) are collaborating to develop an RNA sequencing tool to improve the diagnosis rate of genetic diseases. The tool analyzes RNA data to find aberrant transcripts and mutations, building on KFSHRC's clinical data and KAUST's computational expertise. The team has already solved cases that DNA sequencing alone could not, including a case of a young child with brain damage caused by a recessive gene mutation. Why it matters: This collaboration can improve disease management and preventative services in the region, directly contributing to Saudi Arabia’s national research priority of health and wellness.
A KAUST team developed piRNAi, a gene-silencing tool in nematode worms using synthetic RNA sequences interacting with the piRNA pathway. They successfully silenced genes involved in sex determination and other functions, demonstrating multiplexed gene silencing. The gene silencing lasted for varying durations across generations, up to six generations. Why it matters: This expands the molecular toolkit for gene manipulation and offers potential therapeutic applications in humans, given the presence of the same gene-silencing pathway.
KAUST's Environmental Epigenetics Program (KEEP), led by Prof. Valerio Orlando, focuses on understanding how cells acquire and maintain memory, particularly in response to environmental factors. The research investigates the role of non-coding RNA and chromosomal components in regulating gene expression beyond the DNA sequence. Epigenetics explains how the same genome can be interpreted differently, allowing cells and organs to adapt to changing conditions. Why it matters: This research could provide insights into how environmental factors impact gene expression and cell function, potentially leading to advances in understanding and treating diseases.
This paper presents six experiments evaluating personalization and user tracking in web search engine results. The experiments involve comparing search results based on VPN location (including UAE vs others), logged-in status, network type, search engine, browser, and trained Google accounts. The study measures total hits, first hit, and correlation between hits to identify patterns of personalization. Why it matters: The findings shed light on the extent of filter bubble effects and potential biases in search results for users in the UAE and globally.