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DOI 10.64898/2026.07.09.737600

Медицинская библиотека · bioRxiv : the preprint server for biology · 2026

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

Gorman Brittney L, Bhotika Harsh, Jehrio Matthew, Purkerson Jeffrey M, Carlin Frederic, Nakayasu Ernesto, Dylag Andrew M, Misra Ravi S, Adkins Joshua, Anderton Christopher R, Pryhuber Gloria, Clair Geremy

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Аннотация

Multi-omics and spatial-omics technologies are exploding in use, producing increasingly complex datasets. Existing bioinformatics tools are developing rapidly but fail to fully enforce the FAIR principles, leaving the field vulnerable to escalating issues in computational reproducibility. Here, we introduce a reproducible-by-design paradigm represented in an omics data processing package, RomicsProcessor. At its core, the "Romics_object", which is a self-contained digital artifact that encapsulates the full history of the data from the original data to the fully processed state, capturing the details of the transformative steps and the required dependencies. This architecture ensures that computational workflows are fully portable and reproducible. In this manuscript, we demonstrate RomicProcessor's computational capabilities and scalability on diverse datasets, including bulk proteomics, large-scale multiplexed immunofluorescence, and multi-batch mass spectrometry imaging. Providing a robust framework for truly FAIR Data Principles-based analysis, RomicsProcessor is a blueprint for the next generation of reproducible bioinformatics tools that can dramatically accelerate discovery in multi-omics biology in the era of artificial intelligence.

Для цитирования: Gorman Brittney L, Bhotika Harsh, Jehrio Matthew, Purkerson Jeffrey M, Carlin Frederic, Nakayasu Ernesto, Dylag Andrew M, Misra Ravi S, Adkins Joshua, Anderton Christopher R, Pryhuber Gloria, Clair Geremy. Reproducible-by-design: Romics Processor, a FAIR ecosystem for multi-omics and spatial-omics analysis.. bioRxiv : the preprint server for biology. 2026. DOI: 10.64898/2026.07.09.737600.
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