Genome replication is a stochastic process whereby each cell exhibits different patterns of origin activation and replication fork movement. Despite this heterogeneity, replication is a remarkably stable process that works quickly and correctly over hundreds of thousands of iterations. Existing methods for measuring replication dynamics largely focus on how a population of cells behave on average, which precludes the detection of low probability errors that may have occurred in individual cells. These errors can have a severe impact on genome integrity, yet existing single-molecule methods, such as DNA combing, are too costly, low-throughput, and low-resolution to effectively detect them. We have created a method that uses Oxford Nanopore sequencing to create high-throughput genome-wide maps of DNA replication dynamics in single molecules. I will discuss the informatics approach that our software uses, our use of computational modelling to explain the patterns that we observe, and the questions about DNA replication and genome stability that our methods are uniquely positioned to answer.
Michael Boemo began his academic career studying mathematics at Rutgers University in the United States before completing his PhD at the University of Oxford. He joined Professor Conrad Nieduszynski’s lab at the Sir William Dunn School of Pathology, University of Oxford as a postdoctoral researcher to examine what machine learning and big data can tell us about genome stability. His current work uses machine learning to study DNA replication dynamics at single-molecule resolution. He also created a new programming language that can be used to simulate biological systems quickly and easily.