Wobble Genomics Achieves 3x Faster Analysis with Seqera to Accelerate Biomarker Discovery and Cancer Diagnostics

Aim

To develop methods to identify biomarkers from liquid biopsies to enable early-stage cancer detection and diagnosis and improve cancer care outcomes at scale.

Challenges

Wobble's diagnostic platform generated massive datasets that required substantial computational resources and seamless coordination of multiple specialized bioinformatics tools. The bottleneck stemmed from difficulty accessing sufficient computational resources—particularly memory and CPU—on demand, compounded by the advanced technical expertise required to deploy and maintain complex analytical pipelines. This created a scalability ceiling that limited operational capacity.

Before Nextflow and Seqera, the team had to sacrifice pipeline efficiency by running smaller, sequential processes rather than leveraging full parallelization capabilities. This workaround significantly reduced data throughput while concentrating the technical burden on a small number of skilled team members. When additional computational power was required, scaling up through extra cloud instances drove operational costs substantially higher, creating an unsustainable economic model for processing large-scale transcriptomic analyses.

Solution

• Nextflow: Automated, scalable pipeline orchestration and workflow management to streamline complex bioinformatics processing and reduce manual intervention.
• Seqera: Seamless AWS Batch integration enabling efficient multithreading, scalability across large datasets, and simplified cloud resource management.
• Fusion: Cost optimization through execution checkpoints and AWS Spot Instance interruption mitigation, delivering substantial operational savings.

Results

Utilizing Seqera, Wobble Genomics achieved a significant increase in data processing throughput capacity, 3x faster analysis turnaround times, and 30% reduction in operational costs. These improvements enabled Wobble to process significantly larger RNA datasets while dramatically reducing computational bottlenecks, creating new opportunities for therapeutic target discovery and advancing early cancer detection capabilities.