TopNEXt: automatic DDA exclusion framework for multi-sample mass spectrometry experiments

McBride, R., Wandy, J. , Weidt, S., Rogers, S. , Davies, V. , Daly, R. and Bryson, K. (2023) TopNEXt: automatic DDA exclusion framework for multi-sample mass spectrometry experiments. Bioinformatics, 39(7), btad406. (doi: 10.1093/bioinformatics/btad406) (PMID:37364005)

Abstract

Motivation: Liquid Chromatography Tandem Mass Spectrometry (LC-MS/MS) experiments aim to produce high quality fragmentation spectra which can be used to annotate metabolites. However, current Data-Dependent Acquisition (DDA) approaches may fail to collect spectra of sufficient quality and quantity for experimental outcomes, and extend poorly across multiple samples by failing to share information across samples or by requiring manual expert input. Results: We present TopNEXt, a real-time scan prioritisation framework that improves data acquisition in multi-sample LC-MS/MS metabolomics experiments. TopNEXt extends traditional DDA exclusion methods across multiple samples by using a Region of Interest (RoI) and intensity-based scoring system. Through both simulated and lab experiments we show that methods incorporating these novel concepts acquire fragmentation spectra for an additional 10% of our set of target peaks and with an additional 20% of acquisition intensity. By increasing the quality and quantity of fragmentation spectra, TopNEXt can help improve metabolite identification with a potential impact across a variety of experimental contexts. Availability: TopNEXt is implemented as part of the ViMMS framework and the latest version can be found at https://github.com/glasgowcompbio/vimms. A stable version used to produce our results can be found at 10.5281/zenodo.7468914. Data can be found at 10.5525/gla.researchdata.1382.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: McBride, Mr Ross and Weidt, Dr Stefan and Daly, Dr Ronan and Davies, Dr Vinny and Wandy, Dr Joe and Bryson, Dr Kevin and Rogers, Dr Simon
• Authors: McBride, R., Wandy, J., Weidt, S., Rogers, S., Davies, V., Daly, R., and Bryson, K.
• College/School: College of Medical Veterinary and Life Sciences; College of Science and Engineering > School of Computing Science; College of Science and Engineering > School of Mathematics and Statistics > Statistics
• Journal Name: Bioinformatics
• Publisher: Oxford University Press
• ISSN: 1367-4803
• ISSN (Online): 1367-4811
• Published Online: 26 June 2023
• Copyright Holders: Copyright © 2023 The Authors
• First Published: First published in Bioinformatics 39(7):btad406
• Publisher Policy: Reproduced under a Creative Commons license
• Data DOI: 10.5525/gla.researchdata.1382

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