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TCGA Workflow: Analyze cancer genomics and epigenomics data using Bioconductor packages
Tiago C Silva1, Antonio Colaprico2, Catharina Olsen2
1Department of Genetics, Ribeirao Preto Medical School, University of Sao Paulo, Ribeirao Preto, Brazil; Department of Biomedical Sciences, Cedars-Sinai, Los Angeles, CA, USA.
F1000Research
|March 1, 2017
Summary
This study presents a new workflow for integrating cancer genomics and epigenomics data from major public projects. It enables the identification of functional epigenomic elements linked to cancer, using brain tumors as an example.
Area of Science:
- Genomics and Bioinformatics
- Cancer Epigenetics
- Computational Biology
Background:
- Large-scale genomic and epigenomic datasets are available from The Cancer Genome Atlas (TCGA), The Encyclopedia of DNA Elements (ENCODE), and The NIH Roadmap Epigenomics Mapping Consortium (Roadmap).
- Existing Bioconductor packages often focus on specific data types, lacking a comprehensive tool for integrated analysis across these major public resources.
- There is a need for a unified approach to analyze diverse molecular data for biologically relevant insights.
Purpose of the Study:
- To develop and present a workflow for the integrative analysis of genomic and epigenomic data from TCGA, ENCODE, and Roadmap.
- To demonstrate how to download, process, and analyze TCGA data using Bioconductor packages.
- To identify cancer-associated functional epigenomic elements by integrating ENCODE and Roadmap data.
Main Methods:
- Utilized Bioconductor packages including TCGAbiolinks, AnnotationHub, ChIPSeeker, ComplexHeatmap, pathview, ELMER, GAIA, and MINET.
- Developed a workflow for downloading and preparing TCGA data.
- Integrated epigenomic data from ENCODE and Roadmap to identify functional elements associated with cancer.
- Applied the workflow to analyze low-grade glioma (LGG) versus glioblastoma multiforme (GBM) brain tumor data.
Main Results:
- Successfully extracted biologically meaningful genomic and epigenomic data from TCGA, ENCODE, and Roadmap resources.
- Identified specific functional epigenomic elements associated with different cancer types (LGG vs. GBM).
- Demonstrated the utility of the integrated workflow for cancer epigenome analysis.
Conclusions:
- The presented workflow provides a comprehensive approach for integrative analysis of large-scale cancer genomics and epigenomics data.
- This method facilitates the discovery of biologically relevant epigenomic signatures in cancer.
- The workflow enhances the utility of public data resources for cancer research.
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