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- APOLLO Network
- ARPA-H BDF Toolbox
- Cancer Research Data Commons
- Childhood Cancer Data Initiative
- CIMAC-CIDC Network
- Clinical Trials Reporting Program
- Federated Learning
- NCI-Department of Energy Collaboration
- NCI-Molecular Analysis for Therapy Choice Trial Network
- Real-World Data
- U.S.-EU Artificial Intelligence Administrative Arrangement
- Funding
- Data Sharing
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Collaborations
- APOLLO Network
- ARPA-H BDF Toolbox
- Cancer Research Data Commons
- Childhood Cancer Data Initiative
- CIMAC-CIDC Network
- Clinical Trials Reporting Program
- Federated Learning
- NCI-Department of Energy Collaboration
- NCI-Molecular Analysis for Therapy Choice Trial Network
- Real-World Data
- U.S.-EU Artificial Intelligence Administrative Arrangement
- Resources
- Training
- News & Events
- Funding
- About
![Screenshot of the PANOPLY public tutorial interface that reads, ABOUT THE WORKSPACE PANOPLY Tutorial
PANOPLY is a platform for applying state-of-the-art statistical and machine learning algorithms to transform multi-omic data from cancer samples into biologically meaningful and interpretable results. PANOPLY leverages Terra-a cloud-native platform for extreme-scale data analysis, sharing, and collaboration-to host proteogenomic workflows, and is designed to be flexible, automated, reproducible, scalable, and secure. A wide array of algorithms applicable to all cancer types have been implemented, and we highlight the application of PANOPLY to the analysis of cancer proteogenomic data.
This PANOPLY tutorial provides a tour of how to use the PANOPLY proteogenomic data analysis pipeline, using the breast cancer dataset published in Mertins, et. al.] The input dataset ( tutorial-brca-input.zip file) can be found in the tutorial subdirectory, along with a HTML version of this tutorial.](/sites/default/files/2022-12/FireCloudPANOPLYWorkspace.png)