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.
MBZUAI's Eduardo da Veiga Beltrame is developing machine learning tools for analyzing single-cell RNA sequencing data, which measures RNA in thousands of individual cells. Sequencing costs have decreased faster than Moore's Law, enabling large-scale data collection in biology. RNA sequencing provides insights into gene expression and cellular activity, crucial for personalized medicine. Why it matters: Advancements in single-cell RNA sequencing and ML analysis will accelerate personalized medicine by providing detailed insights into cellular mechanisms and disease pathways.
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.
KAUST researchers organized a week-long workshop on bioinformatics, covering genomics and transcriptomics data analysis. The workshop targeted students, postdocs, and senior researchers, providing hands-on training in coding and analysis using tools like R, Python, and shell scripts. Attendees with little prior computational biology experience were introduced to fundamental concepts and tools for handling large sequencing datasets. Why it matters: The workshop addresses the increasing need for bioinformatics expertise at KAUST and in the region, crucial for advancing research in fields like evolution and complex diseases.
A talk discusses the challenges of single-cell data analysis, such as feature sparsity and the effects of rare cells. AI/ML strategies are uniquely positioned to model this data. ImYoo, a startup founded in 2021, is applying single-cell model architectures for unsupervised discovery of patient groupings and predicting sample-level phenotypical data in autoimmune disease. Why it matters: This highlights the growing application of AI/ML in analyzing single-cell data for population-scale human health studies, an area ripe for innovation and improvement in the Middle East's growing biotech sector.
Researchers at the Rosalind Franklin Institute are using generative AI, including GANs, to augment limited biological datasets, specifically mirtron data from mirtronDB. The synthetic data created mimics real-world samples, facilitating more comprehensive training of machine learning models, leading to improved mirtron identification tools. They also plan to apply Large Language Models (LLMs) to predict unknown patterns in sequence and structure biology problems. Why it matters: This research explores AI techniques to tackle data scarcity in biological research, potentially accelerating discoveries in noncoding RNA and transposable elements.
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.
Professor Eran Segal presented The Human Phenotype Project, a longitudinal cohort study with over 10,000 participants. The project aims to identify molecular markers and develop prediction models for disease using deep profiling techniques including medical history, lifestyle, blood tests, and microbiome analysis. The study provides insights into drivers of obesity, diabetes, and heart disease, identifying novel markers at the microbiome, metabolite, and immune system level. Why it matters: Such large-scale phenotyping initiatives could inform personalized medicine approaches relevant to the Middle East's specific health challenges.