Google DeepMind, Google.org and Sanger Institute to launch new AI consortium for genomics. A news article by the Communications Team 8 Jun 2026 The Wellcome Sanger Institute, Google.org, and Google DeepMind are partnering up to generate large-scale genomic datasets and power AI-driven discoveries. News and blog updates Announced today (8 June) at the AI x BIO conference, the Wellcome Sanger Institute and Google DeepMind, with support from Google.org, have announced a new artificial intelligence (AI) consortium for genomics. Over five years, the consortium will focus on addressing data gaps through strategic data generation and building high-quality AI-ready genomic datasets. These datasets will power the next generation of AI models for biological discovery. The partnership aims to make biology more predictive by developing a framework that will support the generation of AI-ready biological datasets specifically designed to train advanced machine learning models. The collaboration seeks to accelerate scientific discovery and unlock deeper biological and biomedical insights. The organisations will establish a broader consortium focused on generating the biological data required to advance AI in biology and welcome additional collaborators who share their goals. The initiative builds on an existing relationship between the Sanger Institute and Google DeepMind, including previous research collaborations, a joint AI in genomics fellowship, and shared efforts to strengthen global AI capability and capacity in research, including in lower and middle income countries. AI is creating unprecedented opportunities to extract insight from genomic and molecular data. While AI is already being used in genomics, opportunities remain to develop models and datasets for underexplored areas of the life sciences. The datasets that the consortium will generate will provide a foundation for training and evaluating AI models capable of making more accurate predictions about biological processes. Researchers at the Sanger Institute are already applying AI-powered approaches across genomics, from extracting insights from large-scale datasets, integrating information from genes, RNA and proteins, to designing and interpreting experiments. "By combining Sanger's expertise in generating world-leading datasets with Google DeepMind's leadership in artificial intelligence, we have an opportunity to accelerate the generation of biological data specifically designed to power the development of foundational AI models. Through this consortium, we aim to create resources that will be shared widely with the community to enable transformative scientific discoveries and deliver broad impact across the life sciences." Dr Julia Wilson,Chief Innovation and Impact Officer at the Wellcome Sanger Institute "Together with the Sanger Institute, we aim to build the data backbone needed to decode the complexities of biological processes. Ultimately, this could accelerate scientific discovery and unlock entirely new frontiers for researchers worldwide." Dr Pushmeet Kohli, Vice President of AI for Science, Google DeepMind "Addressing the most significant challenges in biology will require collaboration across disciplines, sectors and institutions. We are excited to partner with the Sanger Institute in order to strengthen AI in genomics opportunities and ensure this data can help to accelerate breakthroughs that benefit humanity." Anna Koivuniemi, Head of the Google DeepMind Impact Accelerator "Over the past decade, we've seen how deep learning can transform our understanding of complex biological challenges. With this new consortium, Google.org is supporting the open-access data foundations needed to fuel the next generation of biological AI models. By accelerating the integration of large-scale genomic datasets, we aim to support the global research community in achieving life-saving scientific breakthroughs." Leslie Yeh, Director of Google.org Scientific Progress More information. Google.org and Google DeepMind are committing $5 million per year to support this partnership. Google DeepMind and the Wellcome Sanger Institute share an intention to collaborate over a five-year period, with continuation subject to the terms set out in the agreement.

Genomic tool TRACS improves tracking of microbial transmission patterns. The research, published today (24 April) in Nature Microbiology, describes how the new tool, called TRAnsmision Clustering of Strains (TRACS), uses genomics to distinguish between closely related bacterial strains. In a collaboration between experts at the Peter MacCallum Cancer Centre in Australia, the Wellcome Sanger Institute and the University of Oslo, researchers used the TRACS tool to map the transmission of the SARS-CoV-2 virus, the bacterium that causes pneumonia, Streptococcus pneumoniae, and the malaria parasite, Plasmodium falciparum, across different populations. The team believes the TRACS tool will play an important role in infection prevention, outbreak response, and the development of treatments designed to help the human microbiome fight infection. Being able to track the spread of disease-causing microbes, otherwise called pathogens, using genomics has become a major tool in public health and can help inform new ways to prevent transmission. Additionally, it can help understand more about how lifestyle and environmental factors are involved in the transmission of these pathogens, and how they colonise the human microbiome. Currently, genomic tools used to track multiple bacterial species at once do not have the speed and flexibility required for routine public health monitoring and can struggle to distinguish between samples transmitted recently and those transmitted years ago. Furthermore, it can be difficult to continuously add in new samples, making real-time surveillance difficult. To address this, an international team developed TRACS, a highly accurate and easy-to-use algorithm that can distinguish between two closely related samples and tell whether they are likely to have come from a direct point of transmission or were acquired at the same source. The TRACS algorithm identifies small genetic differences, known as Single Nucleotide Polymorphisms (SNPs), and then analyses these differences to estimate how closely related the pathogens are, and if they are likely to have recently been transmitted. This approach allows for the continuous integration of new samples, making it an ideal tool for accurately identifying transmission networks and ruling out transmission events in ongoing public health applications. In this new study, the team used TRACS to map pathogen transmission networks across three different populations, all of which had different genomic data. They applied it to SARS-CoV-2 data from UK hospitals, deep population sequencing data of Streptococcus pneumoniae and single-cell genome sequencing data from malaria patients infected with Plasmodium falciparum. They found that the tool was able to identify different pathogens in one sample and infer where these were each transmitted. They also used TRACS to study how microbes are passed from mothers to infants and found that one beneficial bacterium, Bifidobacterium breve, persisted in infants longer than previously recognised, something that previous methods have missed. Traditionally, this has been very difficult for AZO Life Sciences to achieve, yet it is incredibly important to know, as people can carry several slightly different versions or strains of the same species at once, which makes it challenging to understand how microbes move between individuals. Using this new technology, AZO Life Sciences can now overcome this challenge and gain a clearer picture of how microbes are shared between people. This will give AZO Life Sciences a better understanding of how microbes spread to help AZO Life Sciences prevent infection in vulnerable populations, like its cancer patients." Dr Gerry Tonkin-Hill, first and corresponding author at the Peter MacCallum Cancer Centre eBook: grow your synthetic biology processes eBook. Synthetic biology has the power to revolutionise science. From its humble beginnings to its current iterations, synthetic biology keeps pushing the boundaries of what is possible. This eBook presents an overview of synthetic biology and how it can be used across disciplines. Dr Trevor Lawley, co-author at the Wellcome Sanger Institute, said: "This research could support the development of new treatments that use beneficial microbes to improve health. By understanding exactly how microbes move between people and which of them are more likely to thrive in their microbiome, we could design better ways to increase helpful gut microbes and investigate whether there are ways to use these to help prevent infections, opening the door to safer healthcare environments and new microbiome-based therapies." Professor Jukka Corander, senior author at the Wellcome Sanger Institute and the University of Oslo, said: "Genomic surveillance has greatly improved our understanding of how infections spread, and has allowed us to apply this knowledge to inform and develop new public health approaches. This new approach can take any complex sequenced samples of microbes, including bacteria, viruses, fungi, and parasites, and infer whether these came from a direct transmission or a shared source. This is an incredible step forward. The method is both computationally more efficient and more accurate than existing methods and is a clear example of how genomics could be used to support public health." Journal reference: Tonkin-Hill, G., et al. (2026) Enhanced metagenomics-enabled transmission inference with TRACS. Nature Microbiology. DOI: 10.1038/s41564-026-02339-x. https://www.nature.com/articles/s41564-026-02339-x Be the first to rate this article

In a recent study, researchers from the Sanger Institute and their collaborators introduced NicheCompass, a deep-learning AI model focused on cell-to-cell communication.