Extracting Accurate Precursor Information for Tandem Mass Spectra by RawConverter

Area of Science:

• Proteomics
• Computational Biology
• Mass Spectrometry Data Analysis

Background:

• Mass spectrometry data analysis relies on accurate extraction from proprietary RAW files.
• High-resolution mass spectrometers provide valuable data for peptide/protein identification and quantification.
• Existing tools may not fully leverage high-accuracy mass data.

Purpose of the Study:

• To develop a software tool for improved data extraction from Thermo Fisher mass spectrometer RAW files.
• To enhance the accuracy of precursor mass-to-charge (m/z) values associated with tandem mass spectra.
• To facilitate downstream peptide and protein identification and quantification.

Main Methods:

• Developed RawConverter, a stand-alone software tool for RAW file data extraction.
• RawConverter extracts full scan and MS(n) data, associating accurate precursor m/z values.
• Supports data-dependent acquisition (DDA) and data-independent acquisition (DIA) with multiple output formats (MS1/MS2/MS3, MGF, mzXML).

Main Results:

• RawConverter significantly improves peptide spectra matches (PSMs) and subsequent peptide/protein identifications compared to ProteoWizard, pXtract, and RawXtract.
• Demonstrated increases in peptide identifications ranging from 17.4% to 45.7% and protein identifications from 13.0% to 36.2%.
• The software accurately associates precursor m/z values, crucial for enhanced identification accuracy.

Conclusions:

• RawConverter is an effective tool for extracting high-quality data from Thermo Fisher RAW files.
• The software enhances the accuracy of mass spectrometry data analysis, leading to improved proteomic identifications.
• Freely accessible software that supports common data formats for broad usability in proteomics research.