Automatic Selection of Search Parameter Values for Mass Spectrometry-Based Search Engines

Area of Science:

• Proteomics
• Computational Biology
• Mass Spectrometry Data Analysis

Background:

• Protein identification and quantification are vital in proteomics.
• Selecting optimal search parameters for mass spectrometry data analysis is challenging and time-consuming.
• Existing search engines require manual parameter tuning, hindering efficient data processing.

Purpose of the Study:

• To introduce QuickSearchProt, an algorithm designed to automate and optimize the selection of search parameters for proteomics data.
• To assist researchers in choosing appropriate search parameters for various search engines.
• To reduce the time and complexity associated with proteomics data processing.

Main Methods:

• Developed an algorithm, QuickSearchProt, that analyzes a small subset of spectra to determine optimal search parameters.
• Integrated considerations for dataset specifications and search algorithm properties.
• Implemented support for X! Tandem and Sage search engines for data-dependent acquisition (DDA) proteomics datasets.

Main Results:

• QuickSearchProt processes most datasets within minutes, irrespective of their original size.
• The algorithm effectively assists in selecting optimal search parameters for proteomics data.
• The implementation is adaptable and scalable for future extensions to more search engines.

Conclusions:

• QuickSearchProt significantly streamlines the proteomics data analysis workflow.
• The algorithm offers a rapid and efficient solution for parameter optimization in mass spectrometry.
• QuickSearchProt is freely available with a GUI and source code for broader accessibility.