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How can omics collaborations and networks align with the FAIR principles for translational research?

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Omics collaborations and networks are essential for advancing translational research, which aims to bridge the gap between basic science and clinical applications. However, to make the most of the vast and diverse data generated by omics technologies, such as genomics, proteomics, metabolomics, and transcriptomics, researchers need to ensure that their data are FAIR: findable, accessible, interoperable, and reusable. In this article, you will learn how to apply the FAIR principles to your omics data and projects, and how to benefit from the existing resources and initiatives that support FAIR omics.

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Nirupama Benis

Assistant Professor at Resuable Health Data lab - AmsterdamUMC, location AMC
Sumeet Pandey, PhD

Translational Immunology | Multi-omics | Datascience
Mbemba Jabbi

Innovator

1 What are the FAIR principles?

The FAIR principles are a set of guidelines that aim to enhance the quality, usability, and impact of data for research and innovation. Developed by a group of experts from different disciplines and sectors in 2016, the FAIR principles emphasize that data should be findable with a unique and persistent identifier, described with rich metadata, and indexed in searchable registries or catalogs. Additionally, data should be accessible by humans and machines, using standard protocols and authentication mechanisms, as well as interoperable with common formats, vocabularies, and ontologies. Additionally, data should be reusable with well-documented provenance information that follows community standards and best practices.

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Mbemba Jabbi

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Given the growing importance of omics datasets relating to human health, the fair principle needs to include clear guidelines for omics studies to aim at include globally representative samples so that human diversity can be better captured in future omics studies, at metaanalytical levels.

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Sumeet Pandey, PhD

Translational Immunology | Multi-omics | Datascience
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??FAIR principles in Omics translational research emphasize Findability, Accessibility, Interoperability, and Reusability of data. ??Findability ensures easy location of data, enhancing its accessibility for researchers. ??Accessibility focuses on making data openly available, fostering collaboration and knowledge sharing. ??Interoperability promotes seamless integration of diverse datasets, facilitating comprehensive analysis. ??Reusability encourages proper documentation and standardization, enabling the utilization of data for various research purposes.

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2 Why are the FAIR principles important for omics data?

Omics data are characterized by their high volume, variety, velocity, and veracity. They can provide valuable insights into the molecular mechanisms, pathways, and biomarkers of health and disease, and enable the discovery and development of new diagnostics, therapies, and prevention strategies. However, omics data also pose significant challenges for data management, integration, analysis, and sharing. Without applying the FAIR principles, omics data can become siloed, inaccessible, incomparable, or obsolete, limiting their potential for translational research.

By following the FAIR principles, omics researchers can enhance the quality, usability, and impact of their data, and facilitate their integration and reuse across different domains and disciplines. For example, by making omics data findable and accessible, researchers can increase their visibility and citation, and foster collaboration and innovation. By making omics data interoperable and reusable, researchers can leverage the power of data integration and meta-analysis, and generate new hypotheses and insights.

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Sumeet Pandey, PhD

Translational Immunology | Multi-omics | Datascience
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??Interoperability: FAIR principles enhance data interoperability, enabling seamless integration of diverse omics datasets. ??Accessibility: Facilitates easy access for researchers, promoting collaboration and accelerating scientific discoveries. ??Reusability: Ensures omics data can be efficiently reused for multiple research purposes, maximizing its value and reducing redundancy. ??Transparency: Promotes transparency in data handling, analysis, and interpretation, fostering trust and reproducibility in omics research.

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3 How to make omics data FAIR?

Making omics data FAIR requires a combination of technical, organizational, and cultural actions. To achieve this, researchers should plan ahead and define their data management plan with objectives, methods, standards, formats, identifiers, metadata, repositories, licenses, and ethical issues. Additionally, they should use common formats, vocabularies, and ontologies for their omics data and metadata as well as follow community standards and best practices for their omics domain. Furthermore, researchers should assign unique and persistent identifiers to their omics data and metadata such as digital object identifiers (DOIs) or accession numbers. Additionally, they should deposit their omics data and metadata in public or domain-specific repositories that support the FAIR principles. Lastly, they should share their omics data and metadata with other researchers and stakeholders using standard protocols and authentication mechanisms as well as promote their data through searchable registries or catalogs.

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4 How to benefit from FAIR omics resources and initiatives?

Omics researchers can benefit from many resources and initiatives that support the FAIR principles. These resources and initiatives can help them discover data from various sources and domains, analyze it using different platforms and frameworks, as well as collaborate with other researchers and stakeholders. For instance, they can use the FAIR Data Point software to create and query FAIR metadata endpoints, the BioSODA platform to query and visualize omics data with semantic web technologies, or the ELIXIR network to access omics infrastructure and services across Europe. Additionally, they can use the FAIRtracks standard to describe and locate genomic tracks, the Galaxy framework to perform reproducible omics workflows, and the Global Alliance for Genomics and Health (GA4GH) to participate in omics standards development and implementation.

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Nirupama Benis

Assistant Professor at Resuable Health Data lab - AmsterdamUMC, location AMC
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This is a great list of resources for FAIR omics data but we must make sure to make the omics community aware of them to use them optimally. The more datasets that are FAIRified and put up on these platforms the more we can do with the data in terms of analyses.

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5 How to get started with FAIR omics?

If you are interested in learning more about the FAIR principles and how to apply them to your omics data and projects, there are a few recommendations. To begin, read the original FAIR paper and the FAIR cookbook for guidance on making your data FAIR. Additionally, explore the FAIRsharing database and OmicsDI to find omics standards, repositories, policies, and tools that support the FAIR principles. Consider taking an online course or workshop on FAIR omics from sources such as the ELIXIR training portal or the GO FAIR training materials. Finally, join a FAIR omics community or initiative like the FAIRplus project or the FAIR4Health consortium to share experiences and feedback.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How can omics collaborations and networks align with the FAIR principles for translational research?

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1 What are the FAIR principles?

2 Why are the FAIR principles important for omics data?

3 How to make omics data FAIR?

4 How to benefit from FAIR omics resources and initiatives?

5 How to get started with FAIR omics?

6 Here’s what else to consider

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