Brandi N. Davis‐Dusenbery

This paper presents an informatic method to identify germline de novo mutations in family trios using consensus across variant callers, achieving high precision and recall.

The paper presents a pan-cancer PDX histology image repository with genomic and pathological annotations for deep learning analysis of tumor regions and xenograft-transplant lymphoproliferative disorder prediction.

This study develops population-specific genome graphs to improve sequencing data analysis, showing reduced mapping errors and better variant calling in African ancestry samples.

The paper describes the PCAWG project's large-scale analysis of cancer whole genomes across multiple computing environments, producing validated consensus variants for downstream research.

The paper introduces Rabix Executor, an open-source workflow engine that enhances reproducibility through reusability and interoperability of workflow descriptions.