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NameEmailPhD ProgramResearch InterestPublications
Lee, Andy

EMAIL

PHD PROGRAM

RESEARCH INTEREST
Bioinformatics, Cancer Genomics, Computational Biology, Genetics

McGee, Teresa

EMAIL

PHD PROGRAM

RESEARCH INTEREST
Bioinformatics, Computational Biology, Genomics

Qiu, Yuechen

EMAIL

PHD PROGRAM

RESEARCH INTEREST
Bioinformatics, Neurobiology

Rogers, Bryan

EMAIL

PHD PROGRAM

RESEARCH INTEREST
Bioinformatics, Cancer Biology, Immunology

Tanikella, Pradham

EMAIL

PHD PROGRAM

RESEARCH INTEREST
Bioinformatics, Computational Biology

Ferris, Marty
WEBSITE
EMAIL
PUBLICATIONS

PHD PROGRAM
Bioinformatics & Computational Biology, Genetics & Molecular Biology

RESEARCH INTEREST
Bioinformatics, Computational Biology, Genetics, Genomics, Immunology, Pathogenesis & Infection, Systems Biology, Virology

In the Ferris lab, we use genetically diverse mouse strains to better understand the role of genetic variation in immune responses to a variety of insults. We then study these variants mechanistically. We also develop genetic and genomic datasets and resources to better identify genetic features associated with these immunological differences.

Love, Michael
WEBSITE
EMAIL
PUBLICATIONS

PHD PROGRAM
Bioinformatics & Computational Biology

RESEARCH INTEREST
Bioinformatics, Computational Biology, Genetics, Genomics

The Love Lab uses statistical models to infer biologically meaningful patterns in high-dimensional datasets, and develops open-source statistical software for the Bioconductor Project. At UNC-Chapel Hill, we often collaborate with groups in the Genetics Department and the Lineberger Comprehensive Cancer Center, studying how genetic variants relevant to diseases are associated with changes in molecular and cellular phenotypes.

Rubinsteyn, Alex
WEBSITE
EMAIL
PUBLICATIONS

PHD PROGRAM
Bioinformatics & Computational Biology

RESEARCH INTEREST
Bioinformatics, Computational Biology, Genomics, Immunology, Translational Medicine, Virology

I work on predicting the determinants of adaptive immune responses. Most of my work has focused on T-cell epitope prediction for mutant antigens derived from cancer. I have collaborated closely with clinical groups to translate this work in personalized cancer vaccine trials. More recently I have also been working on joint T-cell and B-cell prediction for viral pathogens. The technologies and techniques applied across all of my projects are at the intersection of computational immunology, genomics, and machine learning.

Wirka, Robert
WEBSITE
EMAIL

PHD PROGRAM
Cell Biology & Physiology

RESEARCH INTEREST
Bioinformatics, Cardiovascular Biology, Cell Biology, Genetics, Molecular Medicine

Our lab uses human genetics to identify new mechanisms driving coronary artery disease (CAD). Starting with findings from genome-wide association studies (GWAS) of CAD, we identify the causal gene at a given locus, study the effect of this gene on cellular and vessel wall biology, and finally determine the molecular pathways by which this gene influences CAD risk. Within this framework, we use complex genetic mouse models and human vascular samples, single-cell transcriptomics/epigenomics and high-throughput CRISPR perturbations, as well as traditional molecular biology techniques.

Rau, Christoph
WEBSITE
EMAIL
PUBLICATIONS

PHD PROGRAM
Bioinformatics & Computational Biology, Genetics & Molecular Biology

RESEARCH INTEREST
Bioinformatics, Cardiovascular Biology, Computational Biology, Genetics, Genomics, Molecular Biology, Systems Biology, Translational Medicine

Heart failure is an increasingly prevalent cause of death world-wide, but the genetic and epigenetic underpinnings of this disease remain poorly understood. Our laboratory is interested in combining in vitro, in vivo and computational techniques to identify novel markers and predictors of a failing heart. In particular, we leverage mouse populations to perform systems-level analyses with a focus on co-expression network modeling and DNA methylation, following up in primary cell culture and CRISPR-engineered mouse lines to validate our candidate genes and identify potential molecular mechanisms of disease progression and amelioration.