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Reproducible processing of TCGA regulatory networks
Viola Fanfani1, Katherine H Shutta1,2, Panagiotis Mandros1
1Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Biorxiv : the Preprint Server for Biology
|November 22, 2024
Summary
We developed tcga-data-nf, a Nextflow workflow for reproducible cancer regulatory network analysis from The Cancer Genome Atlas (TCGA) data. This tool integrates multi-omics data to infer gene and protein interactions, aiding disease mechanism discovery.
Area of Science:
- Computational Biology
- Bioinformatics
- Genomics
Background:
- Technological advancements enable deep exploration of disease molecular basis.
- Biological networks are crucial for analyzing omics data and modeling gene/protein interactions.
- Large projects like The Cancer Genome Atlas (TCGA) provide rich data for computational method development.
Purpose of the Study:
- To develop a coherent and reusable workflow for end-to-end analysis of cancer regulatory networks.
- To enable reproducible inference of regulatory networks from TCGA multi-omics data.
Main Methods:
- Development of tcga-data-nf, a Nextflow workflow.
- Integration of multi-omics data (RNA-seq, methylation) from TCGA.
- Utilizing netZoo software tools for regulatory network inference.
- Leveraging the NetworkDataCompanion R package for data management.
Main Results:
- The workflow allows reproducible inference of regulatory networks from thousands of TCGA samples via a single command.
- Demonstrated application in studying epigenetic differences between colon cancer subtypes.
- Provided pre-generated networks for the 10 most common cancer types.
Conclusions:
- tcga-data-nf offers a complete, flexible, and extensible framework for cancer regulatory network analysis.
- The workflow ensures reproducible inference and analysis, addressing a gap in existing software tools.
Keywords:
CancerGene Regulatory NetworkNetworkDataCompanionNextflowThe Cancer Genome AtlasreproducibilityMore Related Videos
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