PANOPLY

PANOPLY performs integrative analysis of genomic, transcriptomic, proteomic, and post-translational modification datasets from next-generation sequencing and mass spectrometry-based proteomics to support proteogenomic investigations in cancer.

Key Features:

• Multi-omic integration: Integrates genomic, transcriptomic, proteomic, and post-translational modification data derived from next-generation sequencing and mass spectrometry-based proteomics.
• Statistical and machine learning algorithms: Deploys state-of-the-art statistical and machine learning algorithms to convert complex multi-omic datasets into interpretable results.
• Algorithmic tailoring: Integrates a wide array of algorithms specifically tailored for proteogenomic data processing.
• Automation and standardization: Automates and standardizes proteogenomic data analysis pipelines to enhance reproducibility and efficiency.
• Reproducible workflows: Produces reproducible analyses applicable to cancer proteogenomic datasets.

Scientific Applications:

• Tumor biology characterization: Enables integrated analyses that provide deeper insights into tumor biology.
• Drug target identification: Aids identification of potential drug targets in cancer through proteogenomic integration.
• CPTAC dataset analysis: Applies to Clinical Proteomic Tumor Analysis Consortium (CPTAC) cancer proteogenomic datasets to advance understanding of cancer biology.
• Enhanced interpretability: Improves biological interpretability and relevance of complex multi-omic datasets.

Methodology:

Applies state-of-the-art statistical and machine learning algorithms to integrate genomic, transcriptomic, proteomic, and post-translational modification data from next-generation sequencing and mass spectrometry-based proteomics.