pep2pro: the high-throughput proteomics data processing, analysis, and visualization tool.

Area of Science:

• Proteomics
• Bioinformatics
• Computational Biology

Background:

• High-throughput proteome data analysis presents challenges in integrating and filtering results from various search algorithms.
• Unambiguous assignment of peptides to proteins requires robust data management and analysis pipelines.
• Existing tools may lack the flexibility to handle diverse datasets and complex scientific queries.

Purpose of the Study:

• To develop and present the pep2pro database schema for effective high-throughput proteome data analysis.
• To enable coherent integration and filtering of proteomic data from multiple sources.
• To provide a publicly accessible tool (pep2pro4all) for mass spectrometry data analysis.

Main Methods:

• Designed a relational database schema (pep2pro) for integrating proteomic search results.
• Implemented data filtering for unambiguous peptide-protein identification, including post-translational modifications.
• Developed a data analysis pipeline supporting data export to PRIDE and retrieval via MASCP Gator.
• Released pep2pro4all, including the database schema and scripts for populating with mzIdentML data.

Main Results:

• The pep2pro database schema successfully integrates diverse proteomic datasets and search algorithm outputs.
• The database facilitates thorough data querying, linking samples, spectra, algorithms, and identifications.
• The pep2pro data analysis pipeline has been applied to various Arabidopsis proteome datasets.
• The pep2pro4all release makes the database schema and analysis tools publicly available.

Conclusions:

• The pep2pro database provides a robust framework for high-throughput proteome data analysis.
• Its relational structure supports comprehensive data integration, filtering, and querying.
• The public release of pep2pro4all extends its utility to a broader research community for mass spectrometry data analysis.