The Translational Data Accelerator provides a suite of applications that support researchers with varying levels of technical expertise and through the different stages of the translational research lifecycle.

Whether you are evaluating study feasibility, exploring molecular datasets, building cohorts, conducting advanced analytics, or developing machine learning models, our platforms provide secure access to trusted research data and computational resources.

Atlas

Atlas is the primary self-service application for exploring de-identified clinical data represented in the OMOP Common Data Model. Atlas is built on the OHDSI open-source ecosystem and is widely used by academic medical centers and healthcare organizations around the world.

Atlas enables investigators to:

  • Evaluate study and clinical trial feasibility
  • Explore patient populations
  • Identify potential cohorts
  • Understand disease prevalence and characteristics
  • Define reproducible cohort definitions
  • Support protocol and grant development

The application provides an intuitive web-based interface that allows researchers to answer many common feasibility questions without requiring programming or database expertise. Cohorts developed in Atlas can be used to support requests for research datasets (or biospecimens via TakePART NW) through established institutional governance processes.

Fred Hutch cBioPortal

cBioPortal is an interactive platform for exploring molecular and clinical cancer data and is particularly valuable for researchers seeking to connect molecular findings with clinical outcomes and therapeutic response. Originally developed at Memorial Sloan Kettering Cancer Center and now used by research institutions worldwide, cBioPortal provides powerful visualization and exploration tools for precision oncology research.

The Fred Hutch cBioPortal environment enables researchers to:

  • Explore molecularly characterized patient cohorts
  • Investigate genomic alterations and biomarkers
  • Visualize molecular and clinical relationships
  • Examine treatment and outcome patterns
  • Share and analyze institutional molecular datasets
  • Host and explore investigator-generated research datasets

The Accelerator supports cBioPortal both to provide access to our curated molecular oncology dataset and allow staff to visualize investigator-contributed research studies through established institutional governance processes.

Databricks

Databricks is the primary computational environment supporting advanced analytics, machine learning, and AI-enabled research within the Translational Data Accelerator ecosystem.

The platform provides scalable computing resources and integrated tools for working with structured, unstructured, and multimodal data. Databricks combines data engineering, analytics, machine learning, and generative AI capabilities within a unified environment.

Researchers use Databricks to:

  • Access approved research datasets
  • Perform statistical analyses
  • Develop machine learning models
  • Conduct large-scale computational studies
  • Build reproducible analytical workflows
  • Develop and evaluate AI and LLM-enabled research applications
  • Process complex multimodal datasets

Databricks supports a wide range of users, from biostatisticians and bioinformaticians to data scientists and software engineers. Researchers can work in SQL, Python, and R within a secure, governed environment.