MS1Connect: a mass spectrometry run similarity measure

Author:

Lin Andy1,Deatherage Kaiser Brooke L1,Hutchison Janine R1,Bilmes Jeffrey A2,Noble William Stafford34ORCID

Affiliation:

1. Chemical and Biological Signatures, Pacific Northwest National Laboratory , Richland, WA 99354, USA

2. Department of Electrical and Computer Engineering, University of Washington , Seattle, WA 98195, USA

3. Department of Genome Sciences, University of Washington , Seattle, WA 98195, USA

4. Paul G. Allen School of Computer Science and Engineering, University of Washington , Seattle, WA 98195, USA

Abstract

Abstract 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 these data are not used by MS1Connect. Availability and implementation The MS1Connect software is available at https://github.com/bmx8177/MS1Connect. Supplementary information Supplementary data are available at Bioinformatics online.

Funder

National Institutes of Health

Laboratory Directed Research and Development Program at Pacific Northwest National Laboratory

Battelle for the U.S. Department of Energy

Linus Pauling Distinguished Postdoctoral Fellowship

Pacific Northwest National Laboratory

Battelle Memorial Institute for the United States Department of Energy

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

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