Overview
As mRNA-based therapeutics and vaccines continue to transform modern biotechnology, scaling research and development operations requires robust data infrastructure, traceability, and governance. Fragmented data environments can significantly hinder innovation, decision-making speed, and regulatory readiness.
A leading RNA biotechnology company partnered with Excelra to establish a centralized, FAIR-aligned data platform enabling end-to-end traceability across the mRNA research lifecycle. By leveraging expertise in Scientific Informatics, Scientific Data Management, and FAIR Data principles, Excelra designed a harmonized data foundation spanning discovery, development, and production.
Our client
The client is a leading RNA biotechnology company with a strong track record in advancing mRNA-based vaccines and therapeutics. As part of its continued innovation strategy, the organization sought to expand and scale its R&D initiatives across discovery, development, and manufacturing.
Scaling operations required modernizing legacy data systems and establishing unified traceability across mRNA construct design, experimentation, and production workflows
Client’s challenge
The client was using a legacy GSNAP-based bulk RNA-seq pipeline hosted on an internal server, which lacked compatibility with modern cloud-ready bioinformatics solutions such as AWS. Due to its sequential execution model, the GSNAP alignment process was inflexible, leading to long runtimes, higher failure risk, and increased computational costs, especially when processing low-quality sequencing reads. The pipeline also lacked scalability, fault tolerance, and parallelization capabilities, making it inefficient for large datasets and precision medicine applications. Additionally, it did not support side-by-side benchmarking using alternative transcript quantification tools like Salmon or Kallisto, restricting scientific data management and bioinformatics analysis capabilities. The existing setup was also not user-friendly and limited accessibility across research teams, preventing wider organizational adoption.
Client’s goals
The client aimed to modernize their RNA-seq pipeline by transforming it into a scalable, cloud-enabled, and cost-efficient workflow with enhanced runtime performance and workflow automation. They wanted to redesign the pipeline with Nextflow, incorporating intelligent parallelization, automated failure handling, and support for multiple transcript quantification tools such as Salmon and Kallisto for comparative alignment and expression analysis. A key objective was to make the pipeline cloud-native on AWS, with features like fault tolerance, dynamic resource allocation, and scalable NGS pipeline optimization. They also wanted the solution to be user-friendly, easily deployable, and accessible across the organization to support cross-functional research teams and accelerate biomarker discovery, genomic data interpretation, and computational biology initiatives. Learn more about Excelra’s expertise in workflow modernization through our Computational Biology Services and FAIR Data Solutions.
Our Approach
To address these challenges, Excelra designed a harmonized architecture centered on traceability, interoperability, and scalability.
The approach emphasized:
- A unified platform to monitor and manage all datasets and associated metadata
- Interoperable microservices enabling modular system integration
- A FAIR-aligned semantic data layer leveraging RDF, ontologies, and a knowledge graph
- Centralized identity management and security controls
- End-to-end data flow across research, development, and production stages
The semantic framework leveraged ontology-driven data modeling similar to methodologies described in Ontology and FAIR Data frameworks, enabling standardized relationships across mRNA constructs, batches, and experimental outputs.
Excelra applied expertise in:
- Semantic Data Services
- Scientific Application Development
- Data Mesh Services
to ensure scalability and compliance across the enterprise ecosystem.
Our Solution & Result
The implemented platform delivered a harmonized and traceable data ecosystem.
Key capabilities included:
- End-to-end traceability linking mRNA design rationale, production batches, and experimental outcomes
- AI-powered search and analytics enabling rapid access to contextualized datasets
- Streamlined data review workflows reducing cross-system reconciliation
- Improved data lineage and auditability supporting regulatory compliance
- Reduced reliance on legacy systems, lowering long-term technical debt
The AI-enabled discovery layer aligned with Excelra’s broader AI-driven life sciences solutions, enabling intelligent knowledge retrieval across structured and unstructured data.
Results
Transitioning from fragmented data management to a centralized FAIR-aligned platform produced measurable outcomes:
- ~30–40% reduction in time spent on data review and reconciliation
- Improved traceability across thousands of mRNA constructs and associated production batches
- Faster root-cause analysis and validation of design decisions
- Accelerated R&D cycle times, with workflows completing weeks faster compared to prior manual processes
- Near-complete data lineage coverage across critical R&D workflows
- Reduced compliance risk through standardized metadata and audit-ready traceability
- Increased scientific productivity, allowing researchers to focus on analysis rather than data retrieval
Conclusion
By transitioning from fragmented data practices to a unified, FAIR-aligned data platform, the client established a scalable foundation for mRNA research and development.
The solution resulted in:
- ~30–40% reduction in time spent on data review and reconciliation, driven by centralized access and standardized metadata
- Improved traceability across thousands of mRNA constructs and associated production batches, enabling faster root-cause analysis and decision validation
- Noticeable acceleration in R&D cycle times, with design-to-analysis workflows completing weeks faster compared to prior manual processes
- Reduced compliance risk, with near-complete data lineage coverage across critical R&D workflows
- Higher scientific productivity, as researchers spent significantly less time locating and validating data and more time on analysis and interpretation
Overall, the platform strengthened the client’s ability to scale mRNA R&D operations with greater confidence, consistency, and scientific rigor—supporting faster innovation without compromising data integrity or compliance.
