Client Background
A cancer therapeutics company approached us with the need to manage vast amounts of genomic data generated from their research, which was proving to be costly and time-consuming to store and analyze. Our goal was to enhance data compression ratios, accelerate analytical workflows, and ensure robust data security, thereby enabling more efficient research processes and reducing both time and operational costs.
Challenge
The client’s research involved analyzing complex genomic data to identify potential biomarkers for cancer treatments. The challenge was the massive volume of data, which made storage, transfer, and analysis cumbersome and expensive. They needed a solution that would not only reduce these burdens but also maintain the integrity and accessibility of the data for ongoing cancer research.
Solution
1. Cloud-based Storage and Management
Amazon S3
We implemented Amazon S3 for its high durability and scalability, setting up lifecycle policies to automate archival and retrieval processes, which optimized costs and improved data accessibility.
Hybrid Cloud Computing
We integrated AWS cloud services with our client’s on-premises computational resources using AWS Direct Connect, creating a hybrid environment that supports extensive computational tasks with enhanced flexibility and cost-efficiency.
2. Genomic Data Compression
Advanced Compression Techniques
We applied genomic compression algorithms that included lossless, lossy, and reference-based techniques, tailored to specific data types to achieve an optimal balance between compression ratio, decompression speed, and computational complexity
Quality Score Compression
Utilizing Delta encoding and Quantization, we significantly reduced the volume of quality scores, maintaining essential genomic information while focusing on data variability and statistical relevance.
3. Data Processing Workflow
Alignment/Mapping and Variant Calling
Genomic sequences were aligned to reference genomes, and variants were called using AWS Batch, which efficiently handled the computational demand of these tasks.
Compression and Ingestion
Post-processing, the data was compressed using bespoke algorithms and ingested into Amazon S3, where metadata was managed via AWS Lambda for real-time processing and indexing.
Bespoke QC and Post-Processing
AWS Lambda facilitated on-the-fly quality control checks and additional post-processing tasks to ensure data integrity and readiness for detailed analysis.
Rare-Variant Collapsing Analysis
We leveraged EC2 Spot Instances to perform cost-effective analyses of rare genetic variants, key to understanding genetic diversity and disease mechanisms.
Integration with AWS Services
Managed and automatically scaled computational jobs with optimized resources using AWS Batch.
Used AWS Lambda for sporadic data processing tasks, reducing infrastructure overhead and maximizing responsiveness.
4. Security and Compliance
Data Encryption
We enforced stringent encryption protocols for data at rest and in transit, managed by AWS Key Management Service (KMS), ensuring compliance with HIPAA, GDPR, and other regulatory standards.
Access Control
We crafted detailed IAM roles and policies to implement sophisticated access controls, using AWS’s RBAC and ABAC models to ensure secure and regulated access to sensitive genomic data.
Results
Enhanced Compression Ratios
Our advanced compression strategies significantly reduced storage requirements, decreasing the costs associated with data storage and management.
Accelerated Analysis Pipelines
The streamlined processing of genomic datasets via AWS Batch and Lambda reduced the time from data ingestion to actionable insights, facilitating quicker advances in genomic research.
Improved Security and Compliance
Our enhanced security measures and compliance protocols ensured the integrity and confidentiality of sensitive genomic data, meeting or exceeding industry standards.
Conclusion
Our innovative use of AWS technologies and specialized genomic data compression methods has greatly improved the efficiency and security of genomic data management. This project not only optimized technical operations but also provided economic benefits, enabling our client to advance their cancer research more effectively.