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Jun Prof Lars Barquist

Integrative Informatics for Infection Biology

We develop systems approaches to RNA and infection, using modern visualization, data science, and machine learning technologies to integrate large-scale functional genomics data.

Our research and approach

Recent years have seen accelerating development of high-throughput technologies in infection biology. Now, thousands of genetic loci can be simultaneously interrogated in a single experiment, providing an array of measurements of transcription, translation, regulatory interactions, and fitness effects. The bottleneck in advancing our understanding of pathogens now lies in moving from hypothesis-free screening through data integration to hypothesis generation. We develop new statistical, bioinformatic, and visualization approaches to overcome this bottleneck in the interpretation of complex post-genomic data.

Team members

Research projects

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.

“High-throughput technologies are changing the way biology is done, requiring the development of new computational and statistical tools. We aim to harness these technologies to bring new insights into bacterial pathogenesis.”

Lars Barquist

 

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