AI & MACHINE LEARNING
BESPOKE DATA VISUALISATIONS
CUSTOM SOFTWARE DEVELOPMENT
CLOUD & OPERATIONS
DATA & ANALYTICS
EMBEDDED & ENGINEERING
IOT & CLOUD
Scalable genomics data analysis platform
Supporting the journey towards processing two million genome samples by 2026, our scalable genomics data analysis platform enables seamless data ingestion, real-time processing, and interactive visualisation of genomic information. Developed entirely in the AWS cloud, the solution combines robust infrastructure with sophisticated software technologies to empower scientists with instant access to vital genomic insights, paving the way for breakthroughs in precision medicine.
About the client
The client is a multinational, science-led biopharmaceutical company focused on developing life-changing medicines. Due to confidentiality agreements, the client’s name remains undisclosed (NDA). Committed to innovation in medical research, the client set an ambitious goal to process two million genome samples by the end of 2026.
The challenge of scaling genomic analysis to two million samples
To push the boundaries of genomics research, the client needed a powerful and scalable platform to process two million genome samples by 2026. Traditional processing pipelines were not equipped to handle this volume of data efficiently, leading to delays and potential data bottlenecks. The primary challenge was to build a solution that not only scales seamlessly but also maintains real-time processing capabilities and integration with external laboratory environments.
A critical requirement was to ensure data validation, ingestion, and multi-stage analysis within a highly secure and compliant cloud environment. Additionally, visualisation and analytical tools had to be integrated to allow researchers to explore processed data with ease and accuracy.
Our role in the project
Our engineering team delivered a comprehensive solution that includes:
DevOps services
Maintaining cloud infrastructure, proactively eliminating risks from cloud providers, building and proposing architecture improvements. The solution is entirely cloud-native (AWS) and serverless, utilising AWS Step Functions, Lambda, queues, and Batch for optimal scalability and reliability.
Development and feature enhancement
Implementing bug fixes, refining algorithms, and adding new features to enhance data retrieval and processing accuracy.
Cooperation with third parties
Seamless collaboration with approximately 20 specialists across multiple organisations in a Kanban model, ensuring continuous improvements and rapid feedback loops.
Data orchestration and pipeline management
Implementation of a modular architecture for data ingestion, validation, secondary and tertiary analysis, and output generation.
Visualisation layer
Integration of Apache Superset to allow researchers to explore processed data through interactive dashboards and custom charts.
Key achievements
Scalable processing of genomic data
The solution is designed to handle millions of genome samples efficiently, ensuring high availability and quick processing.
Enhanced real-time visualisation
Researchers now have access to interactive, real-time dashboards powered by Apache Superset, enabling faster decision-making.
Cross-organisational collaboration
Successful coordination of 20+ specialists across different parties, working synchronously in a Kanban-based model.
Compliance and security
Full integration with the client’s IAM tools, ensuring secure, role-based access to sensitive data.
Impact and results
The platform revolutionised the client’s approach to large-scale genomics research, enabling faster identification of genetic mutations and correlations. Insights gained from the platform are paving the way for the development of novel therapies and more efficient treatments for genetic diseases. The project significantly accelerated research timelines and improved data accessibility for scientists worldwide.
COre Technologies
Programming and data science: Python, React, Node.JS, SQL
Data processing and orchestration: AWS Step Functions, Lambda, Batch, queues
Cloud and infrastructure: AWS, Terraform, Kubernetes
Visualisation: Apache Superset
DevOps: Cloud-native, serverless architecture, proactive risk mitigation
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