Summary
This case study details how Ochre Bio transformed a manual image processing workflow into an automated pipeline using Code Ocean. The Ochre Bio team, which specializes in chronic liver diseases, faced challenges due to a dependency on a manual workflow that was time-intensive and limited to a single computational biologist. The process involved image pre-processing in Python, characterizing image tiles with CellProfiler, and aggregating results for visualization in R. The automation of this workflow has significantly improved productivity by allowing computational biologists to focus on other tasks and has made the workflow more robust and faster. Key integrations with technologies such as Amazon S3, AWS Batch, and Nextflow facilitated this transition. The implementation of separate Compute Capsules for various tasks and the creation of a no-code version of the pipeline has democratized access for wet lab scientists, enabling them to run computational pipelines independently. Overall, the automation has enhanced collaboration and visibility across the global team.
