Jun Prof Lars Barquist

We analyse and integrate genomic and post-genomic data to provide insight into bacterial pathogenesis. We have been active in developing bioinformatics techniques to analyze and interpret the results of experiments using high-throughput sequencing, including transposon-insertion sequencing, CLIP-seq, and dual RNA-seq. We are actively developing new algorithms and statistical approaches to these kinds of data to provide insight into both host-microbe interactions and RNA-based regulation in bacterial pathogens.

We have also been active in applying machine learning tools to the evolution of bacterial pathogens using genome sequencing data. By dissecting the architecture of these machines, we have been able to extract new insights into how pathogens adapt to their hosts. We are currently investigating way to incorporate additional layers of information, such as gene expression information and mutations in non-coding RNA, into such models to provide a more comprehensive view of pathogen behavior.