Integrative Informatics for Infection Biology

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, computational, and visualization approaches to overcome this bottleneck in the interpretation of complex post-genomic data.


Our Research

High-throughput functional genomics technologies are providing unprecedented views of cellular behavior at genome scale. Understanding how bacterial and host cells respond to complex processes like infection increasingly requires the integration and unified interpretation of these diverse technologies.

We are using modern data science technologies, including visualization, machine learning, and statistical modeling to extract biological insight from high-throughput genomic and post-genomic data. We have particular interests in using these technologies to understand the effects of RNA-based regulation in bacteria, and non-coding RNA’s role in host-pathogen interactions and the evolution of pathogens.

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