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Updated: May 24, 2025

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms

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Published on: May 9, 2017

Harmonizing and integrating the NCI Genomic Data Commons through accessible, interactive, and cloud-enabled

Ling-Hong Hung^1,2, Bryce Fukuda^1,2, Robert Schmitz^1,2

¹School of Engineering and Technology, University of Washington Tacoma, Tacoma, Washington, USA.

|March 4, 2025

Summary

This summary is machine-generated.

Researchers can now uniformly process cancer data from repositories like the NCI Genomic Data Commons (GDC) using new graphical workflows. This ensures accurate comparisons and enhances data interpretation for genomic and transcriptomic studies.

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Cancer data repositories like the National Cancer Institute (NCI) Genomic Data Commons (GDC) offer valuable datasets for comparative analyses.
Reproducibly processing user data to match GDC standards is challenging due to text-based Standard Operating Procedures (SOPs) that often lag behind code revisions.

Purpose of the Study:

To enhance the utility of the GDC by converting existing SOPs into an accessible and executable format.
To enable reproducible processing of user data and harmonization of datasets across repositories.

Main Methods:

Converted GDC DNA sequencing (DNA-Seq) and mRNA sequencing (mRNA-Seq) SOPs into reproducible, self-installing, containerized, and interactive graphical workflows.
Harmonized raw RNA-Seq datasets from GDC and Genotype-Tissue Expression (GTEx) using the developed graphical workflows.

Main Results:

Developed and disseminated open-source graphical workflows for GDC DNA-Seq and mRNA-Seq pipelines.
Demonstrated the importance of uniform data processing for accurate differential gene expression analysis by harmonizing datasets processed with different methodologies.

Conclusions:

The developed graphical workflows significantly increase the utility of the GDC by enabling researchers to uniformly process custom and existing datasets.
This approach of making analytical methodologies as accessible as the data can be applied to other repositories to improve scientific research impact.