Building Portable and Reproducible Cancer Informatics Workflows for Scalable Data Analysis: An RNA Sequencing...

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Decreasing sequencing costs generate vast high-throughput data.
• Cloud technologies and interoperability standards facilitate data sharing and analysis.
• Genomic data analysis is now accessible beyond institutions with large local computational resources.

Purpose of the Study:

• To demonstrate the analysis of RNA sequencing data using the Seven Bridges Cancer Genomics Cloud (CGC).
• To illustrate how to integrate new computational algorithms into existing workflows on a cloud platform.
• To highlight best practices for reproducible and scalable biomedical computation in the cloud.

Main Methods:

• Utilizing the Seven Bridges Cancer Genomics Cloud (CGC) for data analysis.
• Analyzing RNA sequencing data from the NIH Cancer Research Data Commons (CRDC).
• Developing and implementing command line tools, Docker containers, and CWL descriptions for cloud-based analysis.

Main Results:

• Successful execution of an RNA sequencing analysis on the cloud.
• Demonstration of integrating a new computational algorithm into an existing workflow.
• Highlighting best practices for creating reproducible and massively parallelized biomedical computations.

Conclusions:

• The Cancer Genomics Cloud (CGC) simplifies large-scale genomic data handling.
• Cloud-based platforms promote interdisciplinary collaboration in biomedical research.
• Best practices enable reproducible and efficient analysis of diverse experimental data.