A new collaboration involving researchers from the University of Cambridge, the Wellcome Sanger Institute, and EMBL’s European Bioinformatics Institute (EMBL-EBI) has been awarded up to US $3.5 million to explore the potential of quantum computing for improvements in human health. The interdisciplinary team aims to develop quantum computing algorithms that can speed up the production and analysis of pangenomes, which are new representations of DNA sequences that capture population diversity.
The project, one of 12 selected worldwide for the Wellcome Leap Quantum for Bio (Q4Bio) Supported Challenge Program, sits at the frontiers of research in both biomedical science and quantum computing. The team will design methods to run on emerging quantum computers, tackling one of the most challenging computational problems in genomic science: building, augmenting, and analyzing pangenomic datasets for large population samples.
The Promise of Pangenomics and Quantum Computing
Pangenomics, a new domain of science, demands high levels of computational power. While the existing human reference genome structure is linear, pangenome data can be represented and analyzed as a network called a sequence graph, which stores the shared structure of genetic relationships between many genomes. The team aims to develop quantum computing approaches with the potential to speed up both the key processes of mapping data to graph nodes and finding good routes through the graph.
Quantum technologies are poised to revolutionize high-performance computing by taking advantage of quantum mechanics to enable solutions to problems that are not practical to solve using classical computers. However, current quantum computer hardware is inherently sensitive to noise and decoherence, so scaling it up presents an immense technological challenge.
Dr Sergii Strelchuk, Principal Investigator of the project from the Department of Applied Mathematics and Theoretical Physics, University of Cambridge, said, “The structure of many challenging problems in computational genomics and pangenomics in particular make them suitable candidates for speedups promised by quantum computing. We are on a thrilling journey to develop and deploy quantum algorithms tailored to genomic data to gain new insights, which are unattainable using classical algorithms.”
The potential benefits of this work are huge, as comparing a specific human genome against the human pangenome, instead of the existing human reference genome, gives better insights into its unique composition. This will be important in driving forwards personalized medicine, while similar approaches for bacterial and viral genomes will underpin the tracking and management of pathogen outbreaks.
