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PANOPLY: a cloud-based platform for automated and reproducible proteogenomic data analysis

Nature Methods volume 18, pages 580–582 (2021)Cite this article

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To the Editor — Proteogenomics involves the integrative analysis of genomic, transcriptomic, proteomic and post-translational modification (PTM) data produced by next-generation sequencing and mass spectrometry–based proteomics. Several publications by the Clinical Proteomic Tumor Analysis Consortium (CPTAC) and others have highlighted the impact of proteogenomics in enabling deeper insight into the biology of cancer and identification of potential drug targets1,2,3,4. To encapsulate the complex data processing required for proteogenomics and provide a simple interface to deploy a range of algorithms developed for data analysis, we have developed PANOPLY—a cloud-based Platform for Automated aNd reprOducible Proteogenomic data anaLYsis. A wide array of algorithms have been implemented, and we highlight the application of PANOPLY to the analysis of cancer proteogenomic data.

PANOPLY leverages Terra (http://app.terra.bio)—a Google Cloud–based 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. Using the underlying architecture of Terra, PANOPLY analysis algorithms and methods are implemented as ‘tasks’, interchangeably referred to as ‘modules’. A series of tasks constitutes a ‘workflow’, which represents a pipeline with outputs of one or more tasks feeding into inputs of downstream tasks. Tasks and workflows are defined using the Workflow Description Language (WDL), allowing users to customize pipelines and quickly add their own tasks to the PANOPLY workflow. The central element of PANOPLY is the Terra ‘workspace’—a shareable collection of everything needed for a data analysis project, including data located on cloud storage linked with the workspace, workflows encapsulating algorithms and pipelines, analysis parameters, results, and a data model that organizes sample metadata into collections of participants, samples and sample sets. With versioned workflows, workspaces enable reproducible research by completely encapsulating input data, analysis methods, settings and results in a shareable cloud-based space.

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Fig. 1: Overview of PANOPLY architecture and the various tasks that constitute the complete workflow.

Data availability

Tutorial data are accessible online at Zenodo (https://doi.org/10.5281/zenodo.4728140), GitHub (https://github.com/broadinstitute/PANOPLY) and Terra (https://app.terra.bio/). Data for the CPTAC LUAD and BRCA studies are accessible online at Terra (https://app.terra.bio/). More detailed information is provided in Supplementary Table 1.

Code availability

All code is accessible online at Zenodo (https://doi.org/10.5281/zenodo.4728140), GitHub (https://github.com/broadinstitute/PANOPLY) and Terra (https://app.terra.bio/). More detailed information is provided in Supplementary Table 1.

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Acknowledgements

This work was supported by grants from National Cancer Institute (NCI) Clinical Proteomic Tumor Analysis Consortium grants NIH/NCI U24CA210979 (to D.R.M. and G.G.) and NIH/NCI U24-CA210986 and NIH/NCI U01 CA214125 (to S.A.C.).

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Authors and Affiliations

  1. Proteomics Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    D. R. Mani, Myranda Maynard, Ramani Kothadia, Karsten Krug, Karen E. Christianson, Karl R. Clauser & Steven A. Carr

  2. Cancer Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    David Heiman, Chet Birger & Gad Getz

  3. Massachusetts General Hospital Cancer Center and Department of Pathology, Boston, MA, USA

    Gad Getz

  4. Harvard Medical School, Boston, MA, USA

    Gad Getz

Authors
  1. D. R. Mani
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  2. Myranda Maynard
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  3. Ramani Kothadia
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  4. Karsten Krug
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  5. Karen E. Christianson
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  6. David Heiman
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  7. Karl R. Clauser
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  8. Chet Birger
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  9. Gad Getz
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  10. Steven A. Carr
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Contributions

The project was conceived by D.R.M., S.A.C., G.G. and C.B. Software design, implementation, testing and documentation was executed by D.R.M., M.M., R.K., K.K., K.E.C., D.H. and K.R.C. D.R.M. and K.K. wrote the manuscript, with input and revisions from all authors.

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Correspondence to D. R. Mani.

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The authors declare no competing interests.

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Peer review information Nature Methods thanks the anonymous reviewers for their contribution to the peer review of this work.

Supplementary information

Supplementary Information

Supplementary Table 1

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Mani, D.R., Maynard, M., Kothadia, R. et al. PANOPLY: a cloud-based platform for automated and reproducible proteogenomic data analysis. Nat Methods 18, 580–582 (2021). https://doi.org/10.1038/s41592-021-01176-6

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