Abstract
As the FAIR (Findable, Accessible, Interoperable, Reusable) principles have become widely accepted in the proteomics field, under the guidance of ProteomeXchange and The Human Proteome Organization Proteomics Standards Initiative, proteomics public databases have been providing Application Programming Interfaces for programmatic access. Based on generating logic from proteomics data, we present Patpat, an extensible framework for searching public datasets, merging results from multiple databases to help researchers find their proteins of interest in the vast mass spectrometry. Patpat's 2D strategy of combining results from multiple databases allows users to provide only protein identifiers to obtain metadata for relevant datasets, improving the 'Findable' of proteomics data. The Patpat framework is released under the Apache 2.0 license open source, and the source code is stored on GitHub (https://github.com/henry-leo/Patpat) and is freely available. Supplementary data are available at Bioinformatics online.
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