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Related Experiment Video

Updated: Jun 18, 2026

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

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

Published on: May 9, 2017

Integrating cross-omics research through FAIR Digital Objects with DataPLANT.

Hannah Dörpholz¹, Rüdiger Simon², Björn Usadel^1,3

¹Institute of Bio- and Geosciences (IBG-4: Bioinformatics), CEPLAS, BioSC, Forschungszentrum Jülich, Wilhelm Johnen Straße, Jülich, Germany.

Journal of Integrative Bioinformatics

|June 17, 2026

Summary

This summary is machine-generated.

The Annotated Research Context (ARC) framework structures complex plant science data, like single-cell RNA sequencing, for better interoperability and reproducibility. This approach enhances data FAIRness (Findability, Accessibility, Interoperability, Reusability) and analysis pipeline reusability.

Keywords:

FAIR data NFDI research data management single cell transcriptomics

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Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

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Last Updated: Jun 18, 2026

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

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

Published on: May 9, 2017

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

Area of Science:

Plant Sciences
Bioinformatics
Computational Biology

Background:

Single-cell and spatial transcriptomics in plant sciences produce large, complex datasets.
Structured metadata is crucial for data interoperability and reproducibility in these fields.
Existing data management methods often lack the necessary structure for complex omics data.

Purpose of the Study:

To demonstrate the utility of the Annotated Research Context (ARC) framework for managing complex plant single-cell and spatial transcriptomics data.
To showcase how ARC enhances data interoperability, reproducibility, and reusability.
To integrate barley single-cell RNA sequencing with spatial transcriptomics data using ARC.

Main Methods:

Experimental metadata captured using ISA tables with ontology annotations for machine readability.
Computational analyses wrapped in Common Workflow Language (CWL) scripts for reproducibility.
Integration of barley single-cell RNA sequencing and spatial transcriptomics data within the ARC framework.

Main Results:

The ARC framework successfully managed and integrated heterogeneous plant transcriptomics datasets.
Ontology annotations and CWL scripts ensured machine readability, interpretability, and reproducibility.
The ARC facilitated unambiguous linking of input materials to analysis results, enabling traceability.

Conclusions:

The ARC framework significantly improves the FAIRness and reusability of complex plant single-cell and spatial transcriptomics data.
Standardized and reusable workflows generated via CWL outweigh the initial technical learning curve.
ARC enables robust comparative analyses across diverse datasets by structuring and annotating heterogeneous data effectively.