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 a 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, 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 collaborating 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 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.

Greater Plains Collaborative (GPC) / PCORnet

The Greater Plains Collaborative (GPC) is a new network of 10 leading medical centers in seven states committed to a shared vision of improving healthcare delivery through ongoing learning, the adoption of evidence-based practices, and active research dissemination. The GPC builds on strong research programs at our sites, existing community engagement, informatics infrastructures and data warehouses developed through the National Institutes of Health (NIH) Clinical and Translational Science Awards (CTSA) initiative at most of our sites, extensive expertise with commercial electronic health records (EHR) systems and terminology standardization, and strong working relationships between investigators and healthcare system information technology departments.