Upload structured datasets (CSV, TSV, spreadsheets) directly into Manifold's high-performance index without any specialized data engineering expertise. Our drag-and-drop interface make simple data ingest dead simple.
Automatically detect, correct, and flag common data issues during ingestion. Our system profiles data on upload, catching errors in column headers and data inconsistencies to ensure your datasets are analysis-ready from day one.
Leverage AI to automatically generate meaningful table and column descriptions from the data. Create custom tags and apply them so that researchers can use faceted search downstream. Refine the metadata to create self-documenting datasets that enhance discoverability and enable better AI workflows downstream.
Manifold lets you work with your data in its original schema—no forced conversions or remapping. The platform, including the catalog, AI agent, and cohort builder, natively supports your existing data models, so you can onboard and use your data immediately. If you want greater interoperability, you have the option to map your data to standardized models like OMOP, either within Manifold or using your own external tools. The choice is always yours: use your data as-is, or transform it for broader compatibility—Manifold adapts to your workflow, not the other way around.
In addition to supporting your native data models, Manifold offers ready-made, opinionated destination schemas for common biomedical data types—germline and somatic variants, RNA-seq, biospecimen inventory, clinical data, and more. Use them if you want a fast path to downstream compatibility and best practices for structure, indexing, and annotation. Mapping your data to these schemas is entirely optional, but it eliminates the need to design your own models and ensures seamless integration with Manifold's applications—accelerating your analysis without extra overhead.
Manifold offers a robust and expanding library of pipelines purpose-built for core biomedical systems such as REDCap, OnCore, Cancer Registry, LIMS, and (coming soon) EHR platforms—enabling seamless, API-driven data ingestion. The library also supports molecular file formats including CRAM, VCF, count matrices, and BED. These pipelines accelerate data onboarding, reduce manual effort, and deliver reliable, community-vetted transformations.
Transform your data with dbt, the leading open-source SQL pipeline orchestrator. Build SQL-based workflows for automated execution on new data, scheduled processing (nightly, weekly, etc.), and incremental updates for large-scale genomic datasets. dbt supports both simple and complex transformations, offering maximum flexibility to fit your needs. Our professional services team can help you design and optimize dbt workflows tailored to your research goals.
Every data transformation is automatically tracked and documented, capturing detailed provenance, version history, and audit trails. This ensures complete traceability, so you can trust your results and easily reproduce your research.
Join your data with a comprehensive set of biomedical vocabularies, including ICD-O3, LOINC, RxNorm, NAACR, and more. Our platform loads complete ontological relationships, enabling advanced semantic queries. For genomic data, connect your datasets with leading reference databases such as ClinVar, dbSNP, GnomAD, Reactome and more. This integration empowers researchers to explore data more intuitively, uncover meaningful connections, and accelerate insights across clinical and molecular domains.
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