Probabilistic framework for integration of mass spectrum and retention time information in small molecule identification

Bach, E., Rogers, S. , Williamson, J. and Rousu, J. (2021) Probabilistic framework for integration of mass spectrum and retention time information in small molecule identification. Bioinformatics, 37(12), pp. 1724-1731. (doi: 10.1093/bioinformatics/btaa998) (PMID:33244585)

Abstract

Motivation: Identification of small molecules in a biological sample remains a major bottleneck in molecular biology, despite a decade of rapid development of computational approaches for predicting molecular structures using mass spectrometry (MS) data. Recently, there has been increasing interest in utilizing other information sources, such as liquid chromatography (LC) retention time (RT), to improve identifications solely based on MS information, such as precursor mass-per-charge and tandem mass spectrometry (MS2). Results: We put forward a probabilistic modelling framework to integrate MS and RT data of multiple features in an LC-MS experiment. We model the MS measurements and all pairwise retention order information as a Markov random field and use efficient approximate inference for scoring and ranking potential molecular structures. Our experiments show improved identification accuracy by combining MS2 data and retention orders using our approach, thereby outperforming state-of-the-art methods. Furthermore, we demonstrate the benefit of our model when only a subset of LC-MS features has MS2 measurements available besides MS1. Availability and implementation: Software and data are freely available at https://github.com/aalto-ics-kepaco/msms_rt_score_integration. Supplementary information: Supplementary data are available at Bioinformatics online.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Bach, Eric and Rogers, Dr Simon and Williamson, Dr John
• Authors: Bach, E., Rogers, S., Williamson, J., and Rousu, J.
• College/School: College of Science and Engineering > School of Computing Science
• Journal Name: Bioinformatics
• Publisher: Oxford University Press
• ISSN: 1367-4803
• ISSN (Online): 1367-4811
• Published Online: 27 November 2020
• Copyright Holders: Copyright © 2020 The Authors
• First Published: First published in Bioinformatics 2020
• Publisher Policy: Reproduced under a Creative Commons License

Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
300982		Exploiting Closed-Loop Aspects in Computationally and Data Intensive Analytics	Roderick Murray-Smith	Engineering and Physical Sciences Research Council (EPSRC)	EP/R018634/1	Computing Science