March 2, 2023
Journal Article

MS1Connect: A Mass Spectrometry Run Similarity Measure


Motivation: Interpretation of newly acquired mass spectrometry data can be improved by identifying, from an online repository, previous mass spectrometry runs that resemble the new data. However, this retrieval task requires computing the similarity between an arbitrary pair of mass spectrometry runs. This is particularly challenging for runs acquired using different experimental protocols. Results: We propose a method, MS1Connect, that calculates the similarity between a pair of runs by examining only the intact peptide (MS1) scans, and we show evidence that the MS1Connect score is accurate. Specifically, we show that MS1Connect outperforms several baseline methods on the task of predicting the species from which a given proteomics sample originated. In addition, we show that MS1Connect scores are highly correlated with similarities computed from fragment (MS2) scans, even though this data is not used by MS1Connect. Supplementary information: Supplementary data are available at Bioinformatics online.

Published: March 2, 2023


Lin A., B. Kaiser, J.R. Hutchison, J.A. Bilmes, and W.S. Noble. 2023. MS1Connect: A Mass Spectrometry Run Similarity Measure. Bioinformatics 39, no. 2:Art. No. btad058. PNNL-SA-180587. doi:10.1093/bioinformatics/btad058