Statistical Analysis of Quantitative Peptidomics and Peptide-Level Proteomics Data with Prostar

Area of Science:

• Proteomics
• Bioinformatics
• Computational Biology

Background:

• Mass spectrometry-based proteomics generates large quantitative datasets.
• Traditional analysis workflows aggregate peptide data to infer protein abundance.
• This aggregation can obscure variability and introduce biases, potentially affecting differential analysis accuracy.

Purpose of the Study:

• To introduce extended workflows in Prostar for enhanced label-free proteomics data analysis.
• To detail methods for conducting differential abundance analysis at the peptide level.
• To present strategies for improved protein quantification and identification.

Main Methods:

• Prostar software's original protein-level analysis workflow.
• Implementation of a "peptidomics" workflow for peptide-level differential analysis.
• A second workflow for aggregating preprocessed peptide abundances before protein-level analysis.

Main Results:

• Demonstration that peptide-level analysis can be more accurate than protein-level aggregation.
• Identification of strategies to mitigate data variability and biases inherent in traditional methods.
• Prostar's extended workflows provide more sensitive detection of differentially abundant proteins.

Conclusions:

• Prostar's enhanced capabilities improve the accuracy and sensitivity of label-free proteomics studies.
• Peptide-level analysis in Prostar offers a more robust approach to identifying differential protein expression.
• The updated Prostar protocol facilitates advanced quantitative proteomics research.