PhD Projects » ESR 3: Development of new computational methods for characterisation and analyses of intra-host viral populations

Lara Fuhrmann

I am Lara Fuhrmann and I come from Germany. I obtained my Bachelor’s and Master’s degrees in Mathematics at the University of Bonn, Germany. During my Master’s studies, I learned about the field of computational biology and got very much interested in the application of computational methods in the setting of biology. In the scope of my master thesis, I created a mathematical model to describe the dynamics of communicating cell populations based on single-cell RNA data.

During this period, I got intrigued by using mathematical knowledge and tools to improve the understanding of biological processes. Following this idea, I joined ETH Zurich in Switzerland in November 2020 to work in the field of computational virology.
I am very excited to be part of this interdisciplinary research network, and I am looking forward to broadening my knowledge on viruses and the interactions with their hosts, and to contribute to a better understanding of the processes involved.

I am always up for outdoor activities, in particular running, hiking, and tennis. In winter, I love to go snowboarding in the Swiss Alps.


Field:
Bioinformatics
Host institution:
Swiss Federal Institute of Technology Zurich (ETH), Switzerland
Local supervisor:
Prof. Dr. Niko Beerenwinkel (ETH Zurich)
Local co-supervisor:
Prof. Dr. Karin Metzner (University Hospital Zurich)
Project partner:
ESR 4, ESR 9
Work packages:
WP 1.3 Virus-host interactions
WP 2.1 Microevolution: Virus quasispecies

Niko Beerenwinkel
Karin Metzner

Project description

Viruses exist in their hosts as populations of genetically heterogeneous particles often referred to as viral quasispecies. Intra- host genomic diversity can result in phenotypic heterogeneity, and it has been linked to viral pathogenesis and virulence. NGS can be used to assess the genomic heterogeneity of virus populations in a cost-efficient manner, but this approach is challenging due to sequencing errors and short read length. In this work package, we will develop improved tools for the reconstruction of viral genomic diversity from both short-read (Illumina) and long-read (PacBio, MinION) NGS data, and we will leverage the power of the inferred population structure to inform models of virus micro-evolution, to detect selection, and to correlate population features to viral and host phenotypes.

Specifically, the objectives are:

  • Reconstruction of viral haplotypes (full-length strain) from NGS data (BC) while removing sequencing errors, including application to Drosophila C virus (ESR 9) and Deformed wing virus (ESR 4) quasispecies data.
  • Extension by fitting parametric representations of virus populations (e.g. mutation-selection balance).
  • Detection of patterns of positive or negative selection after viral passaging.
  • Investigation of the correlation between viral quasispecies and (viral and host) phenotypes such as viral load, host immune response and survival, and specific readouts of virus-induced pathology.