MSPypeline: a python package for streamlined data analysis of mass spectrometry-based proteomics

Area of Science:

• Proteomics
• Computational Biology
• Bioinformatics

Background:

• Mass spectrometry-based proteomics is vital in biological and medical research.
• Standardized quality control is essential for reliable and comparable proteomic data analysis.
• Existing workflows often lack comprehensive quality control and standardized data processing.

Purpose of the Study:

• To introduce MSPypeline, a software platform for standardized mass spectrometry proteomics data analysis.
• To facilitate robust quality control, data preprocessing, and statistical analysis of proteomic datasets.
• To enable the identification of differentially expressed proteins for biological insights.

Main Methods:

• MSPypeline imports MaxQuant output tables.
• It generates quality control reports and performs data preprocessing, including normalization.
• The platform conducts exploratory analyses using statistical inference plots.

Main Results:

• Standardized assessment of raw data quality, processing steps, and statistical analyses.
• Generation of customizable figures for data visualization and interpretation.
• Facilitation of the identification of differentially expressed proteins.

Conclusions:

• MSPypeline enhances the reliability and comparability of mass spectrometry-based proteomics studies.
• The platform supports robust data quality assessment and preprocessing.
• It aids in drawing biologically relevant conclusions from proteomic data analysis.