An automated pipeline for high-throughput label-free quantitative proteomics

Area of Science:

• Proteomics
• Computational Biology
• Biotechnology

Background:

• Label-free liquid chromatography-tandem mass spectrometry (LC-MS/MS) is crucial for quantitative proteomics.
• Processing large LC-MS/MS datasets presents computational challenges for accurate peptide and protein quantification.

Purpose of the Study:

• To present a computational pipeline for robust peptide and protein quantification in label-free LC-MS/MS data.
• To introduce enhancements to the OpenMS software framework for processing large-scale proteomic experiments.

Main Methods:

• Development of a computational pipeline using OpenMS tools.
• Implementation of new algorithms for raw data centroiding, feature detection, and alignment.
• Introduction of a novel tool for calculating peptide and protein abundances.
• Validation using small ground-truth datasets and comparison with MaxQuant and Progenesis LC-MS.

Main Results:

• The pipeline effectively quantifies peptides and proteins in label-free LC-MS/MS data.
• Enhanced OpenMS algorithms show competitive or improved performance compared to established methods.
• Successful application to a large, heterogeneous dataset of 58 LC-MS/MS runs.

Conclusions:

• The developed computational pipeline provides an efficient and accurate solution for large-scale quantitative proteomics.
• The enhancements to OpenMS facilitate robust analysis of complex label-free LC-MS/MS experiments.
• This tool aids researchers in large-scale proteomic studies requiring accurate protein quantification.