Chromatographic alignment of LC-MS and LC-MS/MS datasets by genetic algorithm feature extraction

Area of Science:

• Proteomics
• Analytical Chemistry
• Bioinformatics

Background:

• Comparing and combining liquid chromatography-mass spectrometry (LC-MS) datasets requires chromatographic alignment.
• Aligning datasets from different instruments, such as LC-MS and LC-MS/MS, presents a specific challenge.

Purpose of the Study:

• To develop a simple and effective method for aligning LC-MS and LC-MS/MS datasets acquired on separate instruments.
• To demonstrate how this alignment can enhance peptide and protein identification and improve quantitative proteomics.

Main Methods:

• Feature extraction from LC-MS datasets.
• Utilizing a genetic algorithm to fit a piecewise linear function to matched peptides.
• Searching LC-MS data within a few ppm of theoretical masses identified by LC-MS/MS.

Main Results:

• Successful alignment of ion trap LC-MS/MS data with FTICR LC-MS data.
• Demonstrated improvement in peptide and protein identification by combining hybrid datasets.
• Showcased enhanced precision and linear dynamic range in quantitative proteomics using FTICR data.

Conclusions:

• The developed method and msalign software provide a robust solution for aligning diverse LC-MS datasets.
• Combining data from different mass spectrometry instruments can significantly improve proteomic analysis.
• The open-source msalign software facilitates broader application in the scientific community.