My ongoing research interest is the development of computational tools to aid collaborative visualization, interpretation, and discovery of clinically relevant genomic events in patient cancers.
I'm currently working as an NCI postdoctoral research fellow with the Griffith lab at the McDonnell Genome Institute of Washington University in St. Louis, where we are investigating the genomic architecture of cancers.
In a set of related projects, I co-developed multiple open-source, API accessible webtools enabling precision medicine. Among these tools are:
- The Drug-Gene Interaction Database (DGIdb) - consolidates disparate data sources describing drug-gene interactions and gene druggability.
- The Clinical Interpretations of Variants in Cancer Database (CIViC) - community-sourced curation tool for clinical interpretations of cancer variants.
- The Database of Curated Mutations in cancer (DoCM) - an expert-curated list of biologically-relevant mutations in cancer.
I also am the project lead for the Variant Interpretation for Cancer Consortium (VICC) meta-knowledgebase project. In this project, we seek to harmonize the elements comprising clinical interpretations of cancer variants (genes, variants, diseases, drugs, and evidence) to enable comparative analyses and develop standards and best practicing for curating and representing these interpretations. To this end, I am an active participant and co-lead in the Genomic Knowledge Standards workstream of the Global Alliance for Genomics and Health (GA4GH).
In another project, I am investigating the genomics of relapsed and chemo-resistant small cell lung cancers (SCLCs), using a combination of exome and transcriptome sequencing to profile the primary and relapsed tumors of 30 patients with SCLC. This work has led to identification of key genetic alterations that appear to be specific or strongly enriched in chemo-resistant SCLCs (manuscript in Nature Communications).
My Ph.D. work was under the mentorship of Terry Braun at the University of Iowa’s Coordinated Laboratory for Computational Genomics. While there, I studied computational methods for identification of disease-associated variations in exome sequencing. I also developed a method for predicting genetic variants associated with heritable retinal dystrophies, using a novel machine learning strategy (PULP).
Another focus of my work was on improving the accuracy of the CLCG variant detection pipeline. Working with Dr. Edwin Stone at the Institute for Vision Research, we developed a technique for identifying synonymous and intronic variants that were associated with Stargardt’s Disease through integrating patient exome data with control (reference) transcriptome sequence. We also developed a model for improving the specificity of detected variants from our exome sequencing experiments.
Finally, I developed web resources to help characterize healthy and diseased retinal tissue. The ocular tissue database (OTDB) provides a look at expression of 10 tissues of the human eye, while the TRIPOD web tool (unpublished) provides an interface for the study and evaluation of phenotypic and genotypic associations in rare, heritable retinal dystrophies.