We develop general-purpose computational approaches that integrate large-scale, heterogeneous public datasets, leading to the mechanistic understanding of microbial genotypes, phenotypes, and diseases.
Specifically, we focus on two key questions:
- How do microbes adapt to different niches, and how can we link microbial genotypes to phenotypic traits? We use a combination of protein sequence-structure-function relationships, comparative genomics, and machine learning to bridge the genotype-phenotype gap (e.g., antimicrobial resistance, host specificity, microbial pathogenesis, plant-soil-microbe adaptations).
- How do we delineate molecular mechanisms underlying host response to infection and discover host-directed therapeutics? We use comparative transcriptomics, disease-drug signatures, and machine learning to tease out disease-specific host responses towards drug repurposing.
Our methods are generally microbe-, host-, and disease-agnostic. We also release open data/software and easy-to-use web applications for wide use by the biomedical community.
We also focus on community engagement, education, and outreach (check out our orgs, R-Ladies Aurora, R-Ladies East Lansing, Women+ Data Science, and AsiaR.
Computational biology & Bioinformatics | Molecular evolution & phylogeny | Comparative pathogenomics/transcriptomics | Microbial pathogenesis | Antimicrobial resistance | Host adaptation | Infectious diseases | Drug repurposing | Data analysis & visualization | Open science
ESKAPE pathogens | Mycobacteria | Escherichia & Shigella | Campylobacter | other WHO/CDC pathogens || Past interests: Bacillus anthracis | Legionella | Listeria
Is your favorite bug/disease missing? Reach out to us! 👇
🔗 Webpage | 💻 Software | GitHub | Mastodon | | Meet the team | PI 📧 👤 Email the PI | 👥 Email the group
