Dr. Cowell received a MS in Biomathematics with a minor in Mathematics in 1995 from North Carolina State University. In 2000, she received a PhD in Biomathematics with a minor in Immunology, also from North Carolina State University. She spent three years as a postdoctoral fellow in the Department of Immunology at Duke University Medical Center and then became an Assistant Professor in the Department of Biostatistics and Bioinformatics. She was also on the graduate faculty at Duke for the Computational Biology and Bioinformatics Graduate Program. In September 2010, she joined the Biomedical Informatics Division in the Department of Clinical Sciences at UT Southwestern.
Dr. Cowell has built a research program focused on the development of bioinformatics and computational biology methods for studying the immune system and infectious diseases. In particular, her work has focused on the somatic diversification of antigen receptor-encoding genes and the development of computable representations of qualitative biological and clinical information. Within each of these areas, she has developed projects that emphasize methodologic development as well as projects focused on answering specific biological questions.
Somatic Diversification of Antigen-receptor Encoding Genes
Dr. Cowell’s research on the somatic diversification of antigen receptor-encoding genes has included projects focused on V(D)J recombination, somatic hypermutation, and receptor editing. Her current projects in this area include the following:
Dr. Cowell’s group is currently developing VDJServer, a free, publicly available, and open source resource providing a data management infrastructure and suite of interoperable analysis tools for antibody and antigen receptor sequencing data. VDJServer will support all steps in repertoire analysis from data management, to sequence processing and analysis, to repertoire characterization, to statistical comparisons and visualization. VDJServer is designed for use by biologists, clinicians, and bioinformatics researchers and will provide access via an intuitive web interface as well as API access for developers. In addition, source code and data will be available for download and local use.
Modeling Recombination Signal Sequences
Dr. Cowell's group is currently working to improve their earlier models of murine recombination signal sequences (Cowell et al. 2002, Cowell et al. 2003). We are utilizing improved model selection methods, newly available high-throughput data sets assessing recombination, and expanding the models to multiple species. We are applying the models to study the role of RAG-mediated recombination in a variety of biological processes, including repertoire formation, receptor editing, chromosomal translocation, and genome evolution.
Immunoglobulin Repertoire Characteristics as an Early Predictor of Multiple Sclerosis
Dr. Cowell's group is collaboarting with Dr. Nancy Monson on her projects aimed at understanding the role of B cells in the pathogenesis of multiple sclerosis. In particular, the Cowell group is developing the bioinformatic algorithms and software needed to address this question.
Computable Representations of Qualitative Biological and Clinical Information
Dr. Cowell’s research on the development of computable representations of qualitative biological and clinical information has focused on developing methods for the representation of qualitative descriptions of immune responses and infectious diseases and for the use of such representations to enhance algorithms for the analysis of high-throughput data, for the integration of data from disparate resources, and for logical inferencing. Current projects within this area include:
Infectious Disease Ontology
The Infectious Disease Ontology comprises a suite of interoperable ontology modules for the infectious disease domain being developed within the framework of the Open Biomedical Ontologies Foundry. The suite includes a core ontology of terms and relations generally relevant for the infectious disease domain, as well as a set of disease- or pathogen-specific extensions.