MS1Connect: a mass spectrometry run similarity measure

Area of Science:

• Proteomics
• Computational Biology
• Mass Spectrometry

Background:

• Interpreting new mass spectrometry data requires comparing it to existing runs in online repositories.
• Calculating similarity between mass spectrometry runs is challenging, especially when experimental protocols differ.

Purpose of the Study:

• To develop and validate a novel method, MS1Connect, for accurately assessing similarity between mass spectrometry runs.
• To improve the retrieval of relevant previous mass spectrometry data for enhanced interpretation of new datasets.

Main Methods:

• MS1Connect analyzes intact peptide (MS1) scans to compute similarity scores between mass spectrometry runs.
• The method does not require fragment (MS2) scan data for similarity computation.

Main Results:

• MS1Connect demonstrates accurate similarity scoring between mass spectrometry runs.
• The method outperforms several baseline approaches in predicting sample species origin.
• MS1Connect scores show high correlation with similarities derived from MS2 scans.

Conclusions:

• MS1Connect provides a robust and accurate method for mass spectrometry data comparison.
• The software facilitates improved data interpretation by enabling effective retrieval of similar runs.
• MS1Connect is a valuable tool for the proteomics research community, available via GitHub.