Our research is committed to development of open access and open source resources for cancer genome analysis. Research projects cover a wide spectrum of cancer informatics and clinical statistics with an emphasis on translation and application. Specifically, we use computational methods for the analysis of large cancer datasets at the molecular level (DNA, RNA and protein) to identify markers for diagnosis, prognosis and drug response prediction in cancer. We have contributed to the early development of methods for analysis of transcriptional regulation (ORegAnno) and RNA-seq analysis and visualization (Alexa Platform).
The group is engaged in a large number of tumor sequencing projects for AML, breast, liver, lung, and other cancers, investigating primary, relapse and drug resistant tumors. To this end we have worked with others at the McDonnell Genome Institute to develop end-to-end pipelines for clinical cancer sequencing that automate state-of-the-art methods for sequence alignment, somatic variation detection, RNA sequence analysis, and the integration of these data types into user-friendly reports of the most clinically relevant genome and transcriptome changes in a tumor or cohort of tumors (Genome Modeling System). To aid in this effort our group has developed databases, knowledgebases, and web tools for interrogating the druggable genome (DGIDB), driver mutations (DoCM), and interpretations of clinically actionable variants in cancer (CIViC). The group is also actively involved in the identification and scoring of tumor neoantigens and development of related software for design of human cancer vaccines (pVac-Seq).
In addition to our basic and clinical research interests, we are also passionate about the scholarship of teaching and learning. We have made substantial contributions to the training and education of tomorrow's bioinformaticians through our involvement in CBW and CSHL workshops and the BioStars forum. We are currently developing a bioinformatics and clinical informatics training program that takes a practical, hands-on approach to cancer genome analysis for personalized medicine.
We are currently seeking highly talented and motivated graduate students and post-docs with skills that span the spectrum from molecular biologist to software/web developer. For more information, please see Careers.
Research in our lab focuses on applying genomic and information science technologies to personalized medicine in cancer. Projects tend to involve (1) development of new methods, software tools, databases or web resources for the analysis and interpretation of genomic data; or (2) comprehensive genomic/informatics analysis of n-of-1 or tumor cohorts using existing open-source bioinformatic tools (aligners, variant callers, etc.).
Trainees interested in joining the lab may find the following suggestions/resources useful:
|CIViC: Clinical Interpretations of Variants in Cancer|
|Breast Cancer Genomics|
|Breast Cancer Knock-Out Mouse Models (CIP)|
|Comparing Breast Cancer and Xenotransplant Whole Genomes (CIP)|
|Drug Gene Interaction Database (DGIdb)|
|Expanding the Clinical Trial Z1031 for Breast Cancer Tumor Sequencing (CIP)|
|Liver Cancer Genomics|
|Lung Cancer Genomics|
|Melanoma Sequencing Project|
|Mouse Luminal Mammary Tumors (CIP)|
|Washington University Cancer Genome Initiative|