Qualitative and Quantitative Shotgun Proteomics Data Analysis from Data-Dependent Acquisition Mass Spectrometry.

Area of Science:

• Proteomics
• Computational Biology
• Mass Spectrometry

Background:

• Shotgun proteomics analyzes proteoforms via peptide analysis.
• Nanoflow liquid chromatography coupled to tandem mass spectrometry (nLC-MS/MS) is standard for peptide identification.
• Data-dependent acquisition (DDA) is a traditional MS/MS analysis mode.

Purpose of the Study:

• To describe a statistically rigorous computational analysis for shotgun proteomics DDA.
• To enable qualitative and quantitative analysis using free, open-source software.
• To facilitate comparison of protein quantities across experimental conditions.

Main Methods:

• Utilizing MS/MS data for peptide identification.
• Quantifying peptides using the area under the curve of peptide mass/charge over chromatographic elution.
• Combining uniquely mapped peptides to a single protein quantity.
• Employing free open-source software tools for analysis.

Main Results:

• A statistically rigorous method for qualitative and quantitative shotgun proteomics DDA analysis.
• Accurate peptide and protein quantification from MS/MS data.
• Identification of statistically significant protein changes between experimental conditions.

Conclusions:

• The described computational approach provides a robust framework for shotgun proteomics.
• Free open-source tools enable accessible and rigorous proteomic data analysis.
• This method supports comparative proteomic studies and biological pathway analysis.