This offer provides the complete UniProt Knowledgebase (UniProtKB), encompassing both the manually reviewed Swiss-Prot and the unreviewed TrEMBL sections, as a powerful, analysis-ready dataset. The UniProt database is the world's most comprehensive resource for protein data, but its raw format is notoriously complex to use at scale. We solve this by processing the entire database into a clean, normalized, and relational set of tables delivered directly to your Azure environment.

Our product gives you immediate access to:

Core Protein and Gene Data: Detailed information on protein entries, names, and associated genes.
Rich Functional Annotations: In-depth comments covering protein function, catalytic activity, subcellular location, and direct links to human diseases.
Sequence Features and Keywords: Positional data for domains and active sites, along with standardized keywords for easy filtering.
Complete Reference and Lineage: Connections to the original scientific literature (PubMed) and full organism taxonomy for every entry.
Integrated Cross-References: Pre-joined links to over 100 external life science databases, providing a unified view of the research landscape.

Use Cases

Drug Target Identification: Rapidly query and analyze protein functional data and disease associations to identify and validate novel drug targets.
Biomarker Discovery: Perform large-scale analysis of protein expression, function, and pathways to discover new biomarkers for disease diagnosis and prognosis.
AI Model Development: Utilize a massive, analysis-ready dataset to train and validate machine learning models for predicting protein function, structure, and interactions.
Proteogenomics Research: Integrate reliable protein data with genomic datasets to gain a deeper, multi-omic understanding of biological systems and disease mechanisms.

Business Needs

Accelerate R&D Timelines: Reduce the time it takes to move from a research hypothesis to a validated result by eliminating foundational data engineering delays.
Reduce Operational Costs: Lower the total cost of ownership for research data platforms by removing the need for in-house development and maintenance of complex data pipelines.
Improve Research Efficiency: Empower expensive, highly-skilled scientists and bioinformaticians to focus on high-value analysis and discovery, not low-value data preparation.
Enable Advanced AI/ML: Provide the clean, structured, and integrated data foundation required to successfully build and deploy predictive models for drug discovery and development.

Outcomes and Results

Reduced Time-to-Insight: The data preparation phase for protein analytics is reduced from 6+ months to a single day, allowing research to begin immediately.
Lower R&D Expenditure: Eliminates costs associated with the internal engineering effort, cloud compute, and ongoing maintenance of a custom UniProt data pipeline.
Increased R&D Productivity: Research teams are freed from data wrangling, enabling them to conduct more experiments and analyses, leading to a higher output of valuable insights.
Enhanced Predictive Power: Provides a reliable and scalable data backbone that improves the accuracy and success rate of AI/ML initiatives in drug discovery.

Universal Protein Knowledgebase (Uniprot)The Data Developers

Universal Protein Knowledgebase (Uniprot)

The Data Developers

Universal Protein Knowledgebase (Uniprot)

The Data Developers

Universal Protein Knowledgebase (Uniprot)
The Data Developers