The Department of Computational Biology develops innovative computational methods to answer challenging biological questions. Through the integration of dry- and wet-lab approaches, cutting-edge software development, and interdisciplinary collaboration, we generate knowledge and tools to advance the treatment of pediatric cancers and other catastrophic diseases.
Analytical techniques and visualization tools developed through major interdisciplinary efforts like the St. Jude–Washington University Pediatric Cancer Genome Project (PCGP) have led to major findings about pediatric cancer genomes — including novel driver and predisposing mutations — and the nomination of therapeutic strategies.
Our data-driven approaches are now being used for many clinical applications, including cancer genetic predisposition studies and the development of targeted therapies. Some of these are being assessed in St. Jude-led pediatric clinical trials. To advance cures, we freely share our data analysis software and interactive visualization tools with the global scientific community.
Research interests: systems biology, systems immunology, immuno-oncology, single-cell systems, biology, translational oncology, functional genomics
Collaborations: Epidemiology and Cancer Control, Pathology, Oncology, Developmental Neurobiology, Pharmaceutical Sciences, Cell and Molecular Biology, Surgery, Bone Marrow Transplant and Cellular Therapy, Chemical Biology and Therapeutics, Hematology, Structural Biology, Tumor Cell Biology
Research interests: pediatric cancer genomics, non-coding variants, population genetics, clonal evolution, cancer predisposition, immunotherapy
Collaborations: Epidemiology and Cancer Control, Pathology, Oncology, Global Pediatric Medicine, Developmental Neurobiology, Biostatistics, Pharmaceutical Sciences, Cell and Molecular Biology, Bone Marrow Transplant and Cellular Therapy, Immunology, Infectious Disease, Chemical Biology and Therapeutics, Hematology, Structural Biology, Tumor Cell Biology
Solving computational biology problems through software engineering, with an emphasis on infrastructure development, clinical analysis support, and tool sharing
Providing the research community with genomic and clinical data visualization and real-time analysis features through our open-source web service, ProteinPaint
