DeepMind Launches AlphaGenome to Predict How DNA Variants Affect Gene Regulation
DeepMind has launched AlphaGenome, a new artificial intelligence (AI) model that can predict how single DNA variants affect gene regulation across the human genome. The model, now available via API for non-commercial research, marks an advance in understanding the genome’s non-coding regions, that is, areas long considered the “dark matter” of DNA. AlphaGenome can analyse up to 1 million DNA base pairs and delivers high-resolution predictions about thousands of molecular processes, such as where genes begin and end, how RNA is spliced and which proteins bind to DNA. This predictive ability, according to DeepMind, offers a “unifying model” to help scientists better understand gene function and the impact of mutations. “It’s a milestone for the field,” Dr Caleb Lareau of Memorial Sloan Kettering Cancer Centre said in the blog post. “For the first time, we have a single model that unifies long-range context, base-level precision and state-of-the-art performance across a whole spectrum of genomic tasks.” Unlike earlier models such as Enformer and AlphaMissense, which focus primarily on protein-coding regions, AlphaGenome is designed to analyse the remaining 98% of the genome, non-coding regions that regulate gene activity and are often linked to disease. DeepMind claims the model offers a new way to explore these vast areas with unprecedented detail. The architecture combines convolutional layers to detect short patterns, transformer models to capture long-range dependencies and final layers to produce predictions. According to the company, AlphaGenome outperformed top external models in 22 of 24 sequence prediction benchmarks and matched or exceeded others in 24 of 26 variant-effect tasks. In a test case involving T-cell acute lymphoblastic leukaemia (T-ALL), AlphaGenome successfully predicted how specific mutations activate the cancer-related TAL1 gene by creating a new binding site for the MYB protein, replicating a known disease mechanism. The result underscored the model’s potential to link non-coding variants to disease outcomes. “AlphaGenome will be a powerful tool for the field,” Professor Marc Mansour of University College London explained in the post. “Determining the relevance of different non-coding variants can be extremely challenging, particularly to do at scale. This tool provides a crucial piece of the puzzle.” DeepMind acknowledges some limitations. It still struggles with predicting the effects of very distant DNA interactions, over 1 lakh letters apart, and has not been validated for personal genome interpretation or clinical use. Researchers are invited to access AlphaGenome through its preview API and collaborate via DeepMind’s community forum. The company says the model could accelerate discovery across disease research, synthetic biology, and basic science. “We hope AlphaGenome will deepen our understanding of the complex cellular processes encoded in the DNA sequence and drive exciting new discoveries in genomics and healthcare,” DeepMind said in a statement.
The model could help link genetic mutations to diseases like cancer, paving the way for better treatments.
The model could help link genetic mutations to diseases like cancer, paving the way for better treatments.
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Merin Susan John is a journalist at Analytics India Magazine, reporting on the intersection of AI and human capital. She can be reached  at merin.john@aimmediahouse.com