Case study

How the Seattle Hub for Synthetic Biology is Leveraging Code Ocean to Push the Boundaries of Synthetic Biology

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The challenge

The Computational Biology team at the Seattle Hub for Synthetic Biology uses custom scripts tailored to each experiment to handle sequencing data. However, this approach has resulted in a fragmented codebase that complicates maintenance, slows updates, and makes standardization difficult.

Data Transfer Bottlenecks

Manually transferring sequencing data from BaseSpace to AWS S3 is inefficient and risky, and automating this process would improve efficiency, security, and reliability.

Manual, Redundant Analysis Steps

The Computational Biology team’s manual, script-based pre-processing of sequencing data is effective but labor-intensive and repetitive, underscoring the need for a unified, automated workflow to boost efficiency and reduce turnaround time.

Fragmented Codebase and Maintenance Overhead

Custom experiment-specific scripts offer flexibility but have resulted in a fragmented codebase that is increasingly difficult to maintain, update, and standardize as it grows.

Automated sequencing data transfer from BaseSpace to AWS using Code Ocean Capsules

Enabled reusable and parameterized analysis workflows via the App Panel

Packaged sequencing data into Code Ocean Data Assets and scaled pipelines for larger datasets

The Computational Biology team adopted Code Ocean to standardize and automate NGS data processing, enabling consistent, reusable results across experiments:

The results

Reduced manual workload

Automated data handling and reusable workflows now save hours of manual effort per experiment and 15 hours of maintenance per month.

Improved reproducibility and scalability

Analysis pipelines produce traceable results and seamlessly scale to handle increasing data volumes as projects grow.

“Code Ocean is a great platform that keeps all of our computational workflows highly organized, reproducible, and traceable. With its intuitive and easy-to-use interface, it is an ideal platform for our team that includes both software engineers and computational biologists.”