Every week that a research team spends sourcing the right genomic dataset is a week lost from the discovery timeline. Today's data procurement landscape forces organizations through a gauntlet of biobank negotiations, data transfer agreements, format reconciliation, and quality validation. All of this happens across multiple vendors and jurisdictions. This results in months of elapsed time before a single analysis begins.
For pharma programs operating under competitive and regulatory pressure, that delay carries real cost. We’re talking pipeline velocity, opportunity windows, and time to patient impact. Rohtek’s genomic data services give pharma, biotech, and research organizations direct access to curated, pre-analyzed genomic datasets. Browse cohorts. Assess quality. Purchase data - all through a single digital workflow.
Our platform provides access to genomic data from approximately 500,000 population-level samples that have already been analyzed. Researchers can explore datasets organized across multiple dimensions and longitudinal cohorts.
Identify cohorts relevant to specific conditions. Filter by disease type, stage, and molecular characteristics. For example, breast cancer patients within a defined age and stage range carrying specific genomic markers.
Source samples reflect the diversity your research requires. Filter by age, sex, geography, and ancestry to build cohorts that match your study design.
Access datasets spanning whole-genome sequencing, exome data, and associated variant annotations, with quality metrics and provenance documented for every sample.
Whether your use case is target discovery, biomarker validation, population genetics, pharmacogenomics, or AI model training, our catalog is structured to surface the right data for the right question.
Mine population-scale genomic data to identify novel therapeutic targets, validate existing candidates against diverse populations, and build the genomic evidence layer that strengthens your pipeline rationale.
Discover and validate biomarkers across large, well-annotated cohorts. Stratify patient populations by molecular characteristics to design smarter clinical trials and more targeted therapeutics.
Train and validate machine learning models for genomic applications using large, curated, and well-documented datasets. Reduce the data preparation burden that typically consumes the first months of any AI project.
Source the genomic evidence needed to develop, validate, and gain regulatory support for companion diagnostics. Access datasets that span the molecular and demographic diversity your assay must cover.
Conduct large-scale genetic association studies, rare variant analyses, and cross-population comparisons using datasets that reflect real-world diversity — without assembling your own biobank.
Investigate how genetic variation influences drug response across populations. Our datasets enable large-scale pharmacogenomic analyses that individual clinical datasets rarely support.
All data is de-identified and handled in accordance with applicable data protection standards. Our platform architecture enforces access controls and data governance policies at every layer.
Sample origin, processing history, and analytical methodology are recorded for every dataset, supporting the auditability that regulated research demands.
Every dataset has undergone analysis prior to listing. Variant calls, quality scores, and annotation layers are documented and accessible.
Datasets are delivered in formats compatible with widely used bioinformatics tools and pipelines, minimizing integration effort for your team.
