Patpat: a public proteomics dataset search framework

Area of Science:

• Proteomics
• Bioinformatics
• Data Science

Background:

• The FAIR principles (Findable, Accessible, Interoperable, Reusable) are increasingly important in proteomics.
• Public proteomics databases offer Application Programming Interfaces (APIs) for data access.
• Existing methods can be challenging for researchers to navigate vast mass spectrometry datasets.

Purpose of the Study:

• To introduce Patpat, an extensible framework for searching public proteomics datasets.
• To improve the findability of proteomics data by merging results from multiple databases.
• To provide researchers with a streamlined method for identifying proteins of interest.

Main Methods:

• Developed an extensible framework named Patpat.
• Implemented a 2D strategy for combining search results from multiple public proteomics databases.
• Utilized protein identifiers as input for retrieving metadata from relevant datasets.

Main Results:

• Patpat enables efficient searching and merging of data from various proteomics databases.
• The framework successfully improves the 'Findable' aspect of proteomics data.
• Researchers can obtain metadata for relevant datasets using only protein identifiers.

Conclusions:

• Patpat offers a novel solution for navigating and searching large-scale proteomics data.
• The framework enhances data discoverability and accessibility for the research community.
• Patpat supports the broader adoption and application of FAIR principles in proteomics.