RawVegetable - A data assessment tool for proteomics and cross-linking mass spectrometry experiments

Area of Science:

• Biochemistry
• Analytical Chemistry
• Proteomics

Background:

• Chromatography is crucial for shotgun proteomics and cross-linking mass spectrometry.
• Optimizing chromatography is essential for accurate data assessment and quality control.

Purpose of the Study:

• To introduce RawVegetable, a novel software for mass spectrometry data assessment and quality control.
• To provide tools for optimizing chromatography in shotgun proteomics and cross-linking experiments.

Main Methods:

• Development of RawVegetable software with four distinct modules.
• Module A: Charge state chromatogram for ion current display per charge state.
• Module B: XL-Artefact determination for noncovalently associated peptides.
• Module C: TopN density estimation for sampling interval detection.
• Module D: Chromatography reproducibility module for pairwise experiment comparison.

Main Results:

• RawVegetable facilitates independent display of ion currents for each charge state, aiding optimization for highly charged ions and low XIC values.
• The software identifies potential noncovalently associated peptides and detects under/over-sampling in retention time intervals.
• Pairwise comparisons of multiple experiments are enabled for assessing chromatography reproducibility.

Conclusions:

• RawVegetable offers a comprehensive solution for mass spectrometry data quality control.
• The software enhances chromatography optimization and reproducibility assessment in proteomics.
• RawVegetable is freely available for academic use, promoting advancements in the field.