A team of researchers from the University of Cambridge, the Wellcome Sanger Institute, and EMBL’s European Bioinformatics Institute have been awarded $3.5 million to explore the potential of quantum computing in improving human health. The project aims to develop quantum computing algorithms to speed up the production and analysis of pangenomes, which are new representations of DNA sequences that capture population diversity. The project is part of the Wellcome Leap Quantum for Bio (Q4Bio) Supported Challenge Program. Key individuals involved include Dr. Sergii Strelchuk, David Holland, and Dr. David Yuan.
Quantum Computing and Genomics: A New Frontier in Biomedical Research
A new interdisciplinary project is set to explore the potential of quantum computing in the field of genomics, specifically in the creation and analysis of pangenomic datasets. The project, which has been awarded up to $3.5 million, brings together researchers from the University of Cambridge, the Wellcome Sanger Institute, and EMBL’s European Bioinformatics Institute (EMBL-EBI). The team aims to develop quantum computing algorithms that could expedite the production and analysis of pangenomes, which are new representations of DNA sequences that capture population diversity.
The project is one of 12 selected worldwide for the Wellcome Leap Quantum for Bio (Q4Bio) Supported Challenge Program. The initiative is based on the premise that the early stages of any new computational method will benefit most from the co-development of applications, software, and hardware.
The Power and Potential of Pangenomes
The concept of pangenomes emerged as a response to the limitations of the reference human genome sequence, which is based on data from only a few individuals and does not represent human diversity. A pangenome is a collection of many different genome sequences that capture the genetic diversity in a population. This could potentially be produced for all species, including pathogens such as SARS-CoV-2.
Pangenomics, a new domain of science, requires high levels of computational power. Unlike the linear structure of the existing human reference genome, pangenome data can be represented and analysed as a network, called a sequence graph, which stores the shared structure of genetic relationships between many genomes. The process of comparing individual genomes to the pangenome involves mapping a route for their sequences through the graph.
Quantum Computing: A Game Changer in Genomic Analysis
Quantum technologies are set to revolutionize high-performance computing. Unlike classical computing, which stores information as binary bits (either 0 or 1), a quantum computer works with particles that can be in a superposition of different states simultaneously. Information in a quantum computer is represented by qubits (quantum bits), which could take on the value 0, or 1, or be in a superposition state between 0 and 1. This allows quantum computers to solve problems that are not practical to solve using classical computers.
However, current quantum computer hardware is sensitive to noise and decoherence, making scaling up a significant technological challenge. Despite this, significant quantum hardware advances are expected to emerge in the next three to five years.
The Project: Quantum Algorithms for Genomic Data
The team will develop, simulate, and then implement new quantum algorithms using real data. These algorithms and methods will be tested and refined in existing, powerful High Performance Compute (HPC) environments initially, which will be used as simulations of the expected quantum computing hardware. They will test algorithms first using small stretches of DNA sequence, working up to processing relatively small genome sequences like SARS-CoV-2, before moving to the much larger human genome.
The Potential Impact on Human Health and Disease Management
The potential benefits of this work are significant. Comparing a specific human genome against the human pangenome – instead of the existing human reference genome – provides better insights into its unique composition. This will be crucial in advancing personalized medicine. Similar approaches for bacterial and viral genomes will underpin the tracking and management of pathogen outbreaks.
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