High content phenotypic approaches, such as Cell Painting, yield rich data that can reveal important insights into a drug’s mechanism of action or toxicity. In an effort to enable a new data-driven approach to drug discovery, the JUMP-CP Consortium has generated a reference Cell Painting dataset. This dataset, unprecedented in scale, and containing data from ~140,000 genetic and small molecule perturbations, was made public in November last year.
The size and complexity, however, present a significant barrier: to leverage it, considerable data storage space, processing power and data science skills are required. During this webinar, Core Life Analytics CEO David Egan will talk about our experiences with the dataset, and discuss:
- How cloud computing can help overcome some of these challenges;
- How to unravel biological processes in this dataset using a best-practices analytics workflow;
- How this approach can be used to assess the reproducibility of the dataset.
About the dataset:
We used the JUMP Cell Painting datasets (Chandrasekaran et al., 2022b), available from the Cell Painting Gallery on the Registry of Open Data on AWS (https://registry.opendata.aws/cellpainting-gallery/).
Disclaimer: Core Life Analytics is not a member or partner of the JUMP-CP Consortium.