PhD defence by Malte Bjørn Hallgren

Principal supervisor

• Professor Frank M. Aarestrup, DTU Food

Co-supervisor

• Postdoc Philip Thomas Lanken Conradsen Clausen, DTU Food

Examiners

• Senior Researcher Pimlapas Leekitcharoenphon, DTU Food
• Senior Researcher Anette Hammerum, Statens Serum Institut
• Assistant Professor Tim Dallman, Department Population Health Sciences, Utrecht University, The Netherlands

Chairperson at defence

• Associate Professor Saria Otani

Resume
Over the past decade, advancements in genetic sequencing technologies have provided researchers with powerful tools to analyze DNA and RNA at unprecedented levels of detail. One such technology, Oxford Nanopore Technologies (ONT), has gained prominence for its ability to sequence long stretches of genetic material. This capability is particularly valuable in understanding the full structure of complex genomes, which is essential for many areas of biological research and healthcare. However, ONT’s relatively high error rate has presented a significant barrier, limiting its utility in certain precise applications, such as tracking disease outbreaks or analyzing microbial communities.

This Ph.D. thesis aims to address the technical challenges of using ONT sequencing data by developing new computational tools that make the technology more accurate and reliable. The research introduces three novel bioinformatics tools: MINTyper, cgPhylo, and NanoMGT, each designed to enhance the use of ONT reads in various applications related to public health and epidemiology. MINTyper is a tool developed to estimate phylogenetic relationships—essentially genetic family trees—between different organisms, combining data from both ONT and Illumina, a more traditional sequencing platform known for its high accuracy. By developing new methods for managing lower-quality ONT data, MINTyper enables researchers to use this data to track how bacteria evolve and spread during an outbreak, even if the raw data contains errors. Another tool, cgPhylo, focuses on identifying small genetic differences between organisms, using a method that ensures mobile genetic elements, such as plasmids, do not interfere with the results. This is particularly useful when studying bacterial outbreaks, where horizontal gene transfer—genes passed between different species—can distort the true evolutionary history of an organism. Finally, NanoMGT offers a novel approach to analyzing microbial communities in metagenomic samples, where multiple species are present. By establishing thresholds for minor variants, NanoMGT improves the accuracy of identifying different bacterial strains, which is crucial when studying environments with high genetic diversity, such as the human gut or environmental samples.

This research not only demonstrates how ONT data can be effectively used despite its error rate, but also highlights the importance of open science. All the tools developed during this Ph.D. are made freely available to the global scientific community through open-source platforms. This ensures that researchers worldwide can utilize and adapt these tools to address their own epidemiological challenges. The implications of this work are significant for public health. With these new tools, epidemiologists can more accurately track and analyze the spread of infectious diseases. For example, real-time sequencing of samples in outbreak areas—whether from patients, animals, or environmental sources—can provide critical information about how a pathogen is spreading and evolving. This capability is particularly valuable in a world where emerging infectious diseases and pandemics pose a growing threat. These innovations are likely to be particularly impactful in low-resource settings, where traditional sequencing infrastructure is limited but the need for timely and accurate genomic data is crucial.