Research in the Peters lab is focused in three areas, all relating to the development of computational tools to address fundamental questions in immunology.

Starting as a PhD student in 2000, Dr. Peters has worked on the development and validation of tools to analyze and predict which parts of a pathogen, allergen or cancer cell are targeted by immune responses. Identifying these specific molecular targets of immune responses, called epitopes, recognized by diseased individuals opens a path towards the development of diagnostics, vaccines and therapeutics for that particular disease. The tools the Peters lab develops aim to reduce the experimental effort required to identify these targets; computer-based predictions allow researchers to focus on the components most likely to be recognized rather than screening thousands of molecules.

The second research area of the lab is the identification of differences between immune cells in individuals with opposite disease outcomes. Powerful experimental tools have been developed to detect differences in how cells utilize the diverse parts of the genome. The Peters lab is using these tools to characterize how immune cells from diseased individuals differ from healthy individuals. These cells are isolated using disease-specific epitopes (or reagents based on them), so our epitope-identifying algorithms directly aid our disease-focused work. This research helps us understand how the disease develops and identifies potential targets in the genome for interventions to treat or prevent the disease.

Finally, the Peters lab is deeply involved in the development of community standards for knowledge representation to promote interoperability and re-use of data. The Peters and Sette lab maintain the Immune Epitope Database (www.iedb.org), which catalogs all published experiments on immune epitope recognition. This requires transforming free text information from journal publications into a structured format, and to make the information optimally useful, connecting it with information stored elsewhere. Doing this efficiently requires a community consensus on knowledge representation. Dr. Peters’ team is contributing to such consensus building and standardization efforts through active work on scientific community initiatives such as the Ontology of Biomedical Investigations (OBI, http://obi-ontology.org/).