Computational Virology

Our Research

© TWINCORE/Lauber

We are a computational research group that is interested in studying viruses and viral infections using sequence-based approaches. A common theme in our projects is the use of unprocessed data from public sequencing archives to generate and test scientific hypotheses in close collaboration with experimental virologists and clinicians. We develop and apply high-performance computing and deep learning workflows to analyze the vast volumes of data and metadata.

Data-Driven Virus Discovery

A major line of our research is the study of viral genetic diversity and virus evolution. We make use of the vast amount of public sequencing data, which we screen for the presence of viral genomes that have been sequenced as a by-product of sequencing the genome or transcriptome of the sampled organism. A main goal of our research is to comprehensively characterize the eukaryotic virome as well as the total virome of diseased and healthy humans. Moreover, we seek to discover unknown animal viruses that are closely related to pathogenic human viruses and may thus serve as experimental models. We have a special interest in nidoviruses and how they evolved the largest known RNA genomes, up to 64 kilobases. We are coordinating the taxonomic classification of nidoviruses as part of the International Committee on Taxonomy of Viruses (ICTV).

PREDICTORix – a virus-host co-evolutionary framework to quantify zoonotic spillover risk

PREDICTORix is a quantitative, phylogeny-aware framework that estimates zoonotic spillover risk across a broad range of virus families without requiring experimental data on transmission or pathogenicity. By combining global sequence resources (including >150,000 newly discovered RNA virus sequences), explicit modeling of virus–host evolutionary relationships, and dual risk metrics (General and Targeted Spillover Risk), PREDICTORix generates quantitative spillover scores to guide interventions and mechanistic follow-up. Originally developed for human pandemic resilience, the framework can be extended to animal and other host species and helps anticipate potential Pathogen X candidates.

Genetic determinants of severe RSV infection

In close collaboration with RESIST scientists and clinicians from MHH, we study the complex interplay of genetic variation in the human genome to identify genetic changes associated with the course or severity of infection with human respiratory syncytial virus (RSV) in young children. We seek to identify both monogenic and polygenic factors that influence disease severity. To address these and other questions, our local and international collaborators have sequenced the exomes of young children with severe RSV disease and of controls with mild RSV infection. We apply bioinformatics and genetic association analyses to identify likely causal variants in immunity-related genes that can form the basis for experimental follow-up.