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NameEmailPhD ProgramResearch InterestPublications
Webb, Julia

EMAIL

PHD PROGRAM

RESEARCH INTEREST
Bioinformatics, Computational Biology, Genomics

Videgar-Laird, Ryan

EMAIL

PHD PROGRAM

RESEARCH INTEREST
Bioinformatics, Genomics, Immunology

Hamilton, Nolan

EMAIL

PHD PROGRAM

RESEARCH INTEREST
Bioinformatics, Computational Biology, Genetics

Brunk, Elizabeth
WEBSITE
EMAIL
PUBLICATIONS

PHD PROGRAM
Bioinformatics & Computational Biology, Chemistry, Pharmacology

RESEARCH INTEREST
Biochemistry, Bioinformatics, Biophysics, Cancer Biology, Computational Biology, Genomics, Pharmacology, Structural Biology, Systems Biology, Translational Medicine

A growing body of work in the biomedical sciences generates and analyzes omics data; our lab’s work contributes to these efforts by focusing on the integration of different omics data types to bring mechanistic insights to the multi-scale nature of cellular processes. The focus of our research is on developing systems genomics approaches to study the impact of genomic variation on genome function. We have used this focus to study genetic and molecular variation in both natural and engineered cellular systems and approach these topics through the lens of computational biology, machine learning and advanced omics data integration. More specifically, we create methods to reveal functional relationships across genomics, transcriptomics, ribosome profiling, proteomics, structural genomics, metabolomics and phenotype variability data. Our integrative omics methods improve understanding of how cells achieve regulation at multiple scales of complexity and link to genetic and molecular variants that influence these processes. Ultimately, the goal of our research is advancing the analysis of high-throughput omics technologies to empower patient care and clinical trial selections. To this end, we are developing integrative methods to improve mutation panels by selecting more informative genetic and molecular biomarkers that match disease relevance.

Merker, Jason
WEBSITE
EMAIL
PUBLICATIONS

PHD PROGRAM
Pathobiology & Translational Science

RESEARCH INTEREST
Bioinformatics, Cancer Biology, Genomics, Pharmacology, Translational Medicine

Our laboratory is focused on translating novel molecular biomarkers into clinical oncology practice, with the overarching goal of improving the care and survival of patients with cancer. Our group is highly collaborative and applies genomic, genetic, bioinformatic, informatic, statistical, and molecular approaches. Current projects in the laboratory include:

  1. Correlative genomic testing to support clinical trials
  2. Expanded clinical applications of RNA sequencing
  3. Development and application of cell-free circulating tumor nucleic acid assays
Ramos, Silvia
WEBSITE
EMAIL
PUBLICATIONS

PHD PROGRAM
Biochemistry & Biophysics

RESEARCH INTEREST
Biochemistry, Bioinformatics, Molecular Biology, Pathology, Translational Medicine

Our research is focused on RNA-binding proteins and their physiopathological roles. An understudied aspect of human disease is gene regulation by modulation of mRNA function. In our research lab we investigate functional connections between the RNA-binding protein Zinc Finger Protein 36 Like-2 (ZFP36L2 or L2) and human diseases. L2 is a member of the Tris-Tetra-Proline or Zinc Finger Protein 36 (TTP/ZFP36) family of RNA-binding proteins that bind Adenine-uridine-Rich Elements (AREs) in the 3’ untranslated regions of target mRNAs. Upon binding, L2 accelerates mRNA target degradation and/or inhibits mRNA translation, ultimately decreasing the protein encoded by the L2-target mRNA.

We have three particular goals:

  • Determine new specific L2-mRNA targets involved in human diseases.
  • Determine the mechanism(s) by which L2 modulates these novel RNA targets.
  • Determine the physiological consequences of L2 ablation in specific cells types using mouse models and CRISPR/Cas9-mediated knockout system.
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.