Custom Biomedical FAIR Data Analysis in the Cloud Using CAVATICA

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• The biomedical data landscape is increasingly harmonized under Findable, Accessible, Interoperable, and Reusable (FAIR) data principles.
• Cloud-based research offers new avenues for analyzing large, diverse datasets using nonstandard computational pipelines.
• Executing custom cloud analyses can be challenging for researchers lacking advanced computational expertise.

Purpose of the Study:

• To present an accessible and streamlined approach for cloud-based computational analysis on the CAVATICA platform.
• To detail the development of a custom workflow for analyzing whole genome sequences of case-parent trios.
• To enable the detection of sex-specific genetic effects on orofacial cleft risk.

Main Methods:

• Development of a cloud workflow using Docker for software environment containerization.
• Creation of individual analysis tools for each step of the workflow.
• Integration of tools into a Common Workflow Language (CWL) pipeline using a visual workflow editor.

Main Results:

• A custom workflow was successfully developed and implemented on the CAVATICA cloud platform.
• The workflow facilitated the analysis of whole genome sequences to investigate sex-specific genetic effects on orofacial cleft risk.
• The approach proved effective for researchers with basic computational skills.

Conclusions:

• The presented three-component approach (Docker, tool creation, CWL pipeline) simplifies cloud-based biomedical data analysis.
• This method is extendable to various high-throughput analyses and compatible with platforms like BioData Catalyst.
• The approach empowers versatile data reuse and accelerates biomedical discovery in the era of FAIR data.