Accelerate Research with Scalable Data Analysis Tools
High-code and low-code environments to enhance research capabilities and facilitate the analysis of complex research data
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Streamline code development with scalable data-aware compute environments integrated with essential biostatistics and bioinformatics tools
Ask AI agents complex research questions in natural language, generate visualizations, and create cohorts without writing a single line of code
Enhance teamwork with a unified project view that integrates data, code, notes, and comments. Share research securely and efficiently within your team
Navigate complex multimodal data in the semantic space of research using a searchable library of reports about cancer diagnoses, medications, biospecimen availability, procedures, genomic variants, and more.
Explore data in files, tables, and variables using our searchable data catalog. The catalog indexes extensive metadata including descriptions of variables, data dictionaries, lineage, and data transformations. It makes your data FAIR.
Quickly assess the feasibility of hypotheses and build cohort datasets with an intuitive AI-powered natural language interface.
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Spin up with one click scalable, pre-configured environments in R, SAS, Python, and Nextflow connected to your data—no need for complex devops.
Collaborate seamlessly with other researchers using projects that integrate research data, transformations, notes, and comments in a single, shareable context.
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Interact with cancer data using an AI-powered, chat based user interface. The agent is — fine-tuned the idiosyncrasies of cancer and your specific data, eliminating the need for coding to answer many questions.
Benefit from a scalable, modern data infrastructure optimized for cancer research, with data harmonization and semantic annotations that drastically reduce data wrangling and boost research efficiency.
Query genetic variants in seconds from our optimized representation. Transform raw sequencing data with our advanced pipelines, powered by Nextflow and AWS Batch.
Support reproducible scientific collaboration with our platform using tools like Git and a shared digital lab notebook. Collaborate privately and, when ready, publish to your organization securely.
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