The Department for “Computational Biology of Infection Research“ at HZI offers Bachelor- /Master-Projects on the topic of “Evolution of the Influenza Virus”. We are looking for highly motivated students of bioinformatics, computer science or similar to work on the problems outlined below.
Area of research
Endemic influenza by estimates causes 500.000 deaths each year, in particular among young children and the elderly. The virus is a single-stranded, negative-sense RNA virus of the family Orthomyxoviridae with a genome consisting of eight segments. The antigenic properties of a specific virus are defined by the surface glycoproteins, haemagglutinin (HA) and neuraminidase (NA). These proteins are under ongoing selection for change and continuously acquire mutations which result in reduced immune recognition in previously infected or vaccinated hosts -- a process referred to as antigenic drift.
The segmented nature of the influenza genome allows the exchange of genome segments between co-circulating viruses - called reassortment event - leading to antigenic and other genetic differences. Reassortant strains resulting from such changes can provide a fitness advantage relative to previously predominating strains are antigenically novel to the host and increase the hosts’ susceptibility. Reassortment events, also known as reticulation events, result in discordant evolutionary histories for different genetic regions.
1. We offer two projects that focus on the simulation of viral evolution and geographic migration. The goal is to implement a simulator that generates genome sequence sets for viral strains to be used as benchmarking data for different evolutionary scenarios. The projects include working with statistical and tree inference methods, tree structures and learning concepts from phylogenetics and population genomics.
- Development of a sequence simulator to generate data to benchmark inference methods that detect selection events providing an evolutionary advantage.
- Development of a sequence simulator to generate data to benchmark methods that infer geographic migration patterns of viruses from genomic and geographic data.
2. A third project focusses on detecting and analyzing topological pattern on phylogenetic trees (trees that represent the evolutionary relationships) that are indicative of providing a selective advantage to some of the represented individual viruses. This project builds upon project number one and includes working with tree structures and data mining methods.
- Detection of topological patterns of trees as criteria for selection in the viral population
- Computational Biology of Infection Research - Prof. Dr. Alice McHardy
Qualified applicants with a disability will be given preference.
As soon as possible.
If you are interested to work on one of these projects or something related please contact Thorsten Klingen via email: email@example.com