A Second Look at FAIR in Proteomic Investigations

Area of Science:

• Proteomics
• Bioinformatics
• Data Science

Background:

• Proteomics aims for comprehensive, large-scale protein analysis linked to phenotypic data.
• Current proteomics scope often extends beyond single experiments.
• Numerous resources exist to improve data findability, accessibility, interoperability, and reusability (FAIR), but adoption is limited.

Purpose of the Study:

• To highlight strategies for increasing the impact of proteomics data beyond individual studies.
• To demonstrate how linking data can unify disparate points and uncover novel relationships.
• To explain how to make datasets and methods more linkable to maximize value.

Main Methods:

• Linking specific terminologies, identifiers, and text to unify data points.
• Developing strategies to render datasets and methods more linkable.
• Discussing the benefits of data linking for various stakeholders.

Main Results:

• Linking data across studies can unify individual data points.
• This approach has the potential to reveal novel biological relationships.
• Making data and methods linkable maximizes their value and impact.

Conclusions:

• Strategies for linking proteomics data are crucial for expanding its impact.
• Enhanced data linkability benefits researchers, data scientists, and other stakeholders.
• Adoption of data linking strategies can foster new discoveries in proteomics.