Reproducible-by-design: Romics Processor, a FAIR ecosystem for multi-omics and spatial-omics analysis

Area of Science:

• Bioinformatics
• Computational Biology
• Data Science

Background:

• Multi-omics and spatial-omics technologies generate complex datasets, posing challenges for computational reproducibility.
• Current bioinformatics tools often fail to fully enforce FAIR principles, leading to reproducibility issues.
• The increasing complexity of omics data necessitates robust and reproducible analysis frameworks.

Purpose of the Study:

• To introduce RomicsProcessor, a novel omics data processing package.
• To establish a reproducible-by-design paradigm for bioinformatics workflows.
• To ensure computational reproducibility and data integrity in multi-omics research.

Main Methods:

• Development of RomicsProcessor, featuring the "Romics_object" artifact.
• The "Romics_object" encapsulates data history and processing dependencies.
• Demonstration of RomicsProcessor's capabilities on diverse omics datasets.

Main Results:

• RomicsProcessor ensures computational workflows are fully portable and reproducible.
• Scalability and computational capabilities were validated on bulk proteomics, multiplexed immunofluorescence, and mass spectrometry imaging data.
• The package provides a robust framework for FAIR Data Principles-based analysis.

Conclusions:

• RomicsProcessor offers a blueprint for next-generation reproducible bioinformatics tools.
• It significantly accelerates discovery in multi-omics biology, especially in the AI era.
• The package enhances data integrity and computational reproducibility for complex omics data.