psm_utils: A High-Level Python API for Parsing and Handling Peptide-Spectrum Matches and Proteomics Search Results
Ralf Gabriels1,2, Arthur Declercq1,2, Robbin Bouwmeester1,2
1VIB-UGent Center for Medical Biotechnology, VIB, 9052 Ghent, Belgium.
Proteomics search engine output files lack standardization, complicating data analysis. The psm_utils Python package unifies these files, offering user-friendly Python, command-line, and web interfaces for easier processing and analysis.
Area of Science:
- Proteomics
- Bioinformatics
- Computational Biology
Background:
- Numerous proteomics search engine output file formats exist, hindering standardized downstream processing of peptide-spectrum matches (PSMs).
- Existing standards for PSM file formats are not universally adopted by search engines.
- A gap exists in software for parsing diverse PSM files into a unified data structure.
Purpose of the Study:
- To introduce psm_utils, a Python package designed to address the lack of standardization in proteomics data processing.
- To provide a unified and user-friendly solution for handling various PSM file formats.
- To facilitate downstream analysis of peptide-spectrum matches.
Main Methods:
- Development of a Python package (psm_utils) for reading and writing diverse PSM file formats.
- Implementation of unified data structures for peptidoforms, PSMs, and PSM lists.
- Creation of Python API, command-line interface, and a web application for PSM file interconversion and statistics.
Main Results:
- psm_utils successfully parses and unifies data from various PSM file formats.
- The package offers accessible Python, command-line, and web interfaces for data handling.
- A web application enables easy PSM file interconversion and basic statistical analysis.
Conclusions:
- psm_utils provides a pragmatic and maintainable solution for standardizing proteomics data processing.
- The package enhances the usability of PSM files through unified data structures and multiple interfaces.
- psm_utils promotes broader accessibility and efficiency in proteomics data analysis.
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