Cancer Genomics Research Laboratory
The Cancer Genomics Research Laboratory (CGR) investigates the contribution of germline and somatic genetic variation to cancer susceptibility and outcomes in support of DCEG's research. Working in concert with epidemiologists, biostatisticians and basic research scientists in DCEG’s intramural research program, CGR provides the capacity to conduct genome-wide discovery studies and targeted regional approaches to identify the heritable determinants of various forms of cancer.
CGR's high throughput laboratory is equipped with state-of-the-art laboratory equipment and automation systems for a large number of applications. CGR supports DCEG in all stages of cancer research from planning to publishing, including experimental design and project management, sample handling, genotyping and sequencing assay design and execution, development and implementation of bioinformatic pipelines, and downstream scientific research and analytical support.
Utilizing the newest technologies, CGR conducts scientific research in support of DCEG investigators via genomic and analytical resources across a variety of applications, including:
- DNA extraction and sample handling services
- Design, execution and analysis for genome wide association studies
- Validation and replication utilizing targeted genotyping techniques
- Epigenetic studies utilizing array-based methylation analysis
- Telomere length assessment
- Evaluation of copy number variation
- Whole exome sequencing and analysis for familial and population germline studies
- Regional and targeted sequencing applications
Wagner S, Roberson D, Yeager M, et al. Development of the TypeSeq assay for detection of 51 human papillomavirus genotypes by next-generation sequencing. J Clin Microbiol 2019 May (Epub ahead of print).
Wagner S, Roberson D, Boland J, et al. Evaluation of TypeSeq, a novel high-throughput, low-cost, next-generation sequencing-based assay for detection of 51 human papillomavirus genotypes. J infect Dis 2019 Jun (Epub ahead of print).
Dawson ET, Wagner S, Roberson D, et al. Viral coinfection analysis using a MinHash toolkit. BMC Bioinformatics 2019 Jul.
Gouveia MH, Bergen AW, Borda V, et al. Genetic signatures of gene flow and malaria-driven natural selection in sub-Saharan populations of the "endemic Burkitt Lymphoma belt". PLoS Genet 2019 Mar.