Biology subjects

Wandy, J.

Publications and source records attributed to Wandy, J..

10 recordsLinked to original sources

bioRxiv2025

Large-scale discovery and annotation of hidden substructure patterns in mass spectrometry profiles

Untargeted mass spectrometry measures many spectra of unknown molecules. To annotate them, tandem mass spectrometry (MS/MS) generates fragmentation patterns representing common substructures. MS2LDA discovers these patterns via unsupervised topic modelling as Mass2Motifs. However, MS/MS-based substructure identification is limited by computational efficiency and interpretability. Here, we report an up to 14x speed improvement through improved algorithmic efficiency. Furthermore, the new automated Mass2Motif Annotation Guidance (MAG) aids in structurally identifying Mass2Motifs. Using three chemically diverse curated MotifDB-MotifSets for benchmarking, MAG achieved median substructure overlap scores of 0.75 up to 0.93, demonstrating robust substructural annotations. We further validated MS2LDA 2.0 in experimental data by identifying substructures of pesticides spiked into a biological matrix and demonstrated its discovery potential by annotating previously uncharacterized fungal natural products. Together with the new visualization app and MassQL-searchable MotifDB, we anticipate that MS2LDA 2.0 will boost the identification of novel chemistry and hidden patterns in mass spectrometry profiles.

biochemistry↗

bioRxiv2025

Comprehensive LC-MS/MS Data Acquisition in Metabolomics via Maximum Bipartite Matching

BackgroundIn untargeted metabolomics studies, liquid chromatography tandem mass spectrometry (LC-MS/MS) is a powerful analytical platform. The fragmentation spectra produced can be used as "molecular fingerprints" to identify unknown metabolites. However, the high number of analytes that may be co-eluting limits the number of fragmentation spectra that can be collected and potentially identified, presenting a serious bottleneck for many studies. There is a need for new fragmentation strategies which are comprehensive, interpretable and robust, meaning they produce high-quality fragmentation spectra for as many analytes as possible while operating within the constraints of notoriously noisy mass spectrometry data. ResultsWe present a data acquisition workflow which uses a bipartite graph to represent the relationship between opportunities for fragmentation and desired fragmentation targets. This method allows a schedule for data acquisition to be optimally allocated by a standard algorithm. We augment this existing technique by allowing it to solve for multiple samples collectively, allowing it to optimise target intensity (and hence spectral quality) via the use of a weighted matching and by assigning leftover scans redundantly to improve robustness. We also show how this workflow can be used flexibly to generate inclusion windows for Data-Dependent Acquisition (DDA) methods. Our experiments show that several thousand peaks identified in a realistic biological sample can be targeted using only two LC-MS/MS runs. We also further investigate the trade-off between offline workflows and DDA methods by exposing our target list of peaks to realistic variation across samples. We find in those circumstances that our new method has performance (measured by number of peaks targeted comparable to state-of-the-art DDA methods). However, this competitive performance is only possible with our additions to the base maximum matching technique, which provide extra resistance against inter-sample variations. ConclusionsWe have proposed a workflow for LC-MS/MS data acquisition which can be used flexibly for entirely pre-scheduled acquisition or which may generate inclusion windows for online DDA methods. Our results show that the maximum matching workflow with our improvements is state-of-the-art where pre-scheduling is concerned, and in future this foundation may be developed to build more powerful DDA methods which can action the promise of truly comprehensive data acquisition.

bioinformatics↗

bioRxiv2023

TopNEXt: Automatic DDA Exclusion Framework for Multi-Sample Mass Spectrometry Experiments

MotivationLiquid Chromatography Tandem Mass Spectrometry (LC-MS/MS) experiments aim to produce high quality fragmentation spectra which can be used to identify 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. ResultsWe 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. AvailabilityTopNEXt 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. Contactr.mcbride.1@research.gla.ac.uk or vinny.davies@glasgow.ac.uk Supplementary informationSupplementary data are available at Bioarxiv online.

molecular biology↗

bioRxiv2023

Simulated-to-real Benchmarking of Acquisition Methods in Metabolomics

Data-Dependent and Data-Independent Acquisition modes (DDA and DIA, respectively) are both widely used to acquire MS2 spectra in untargeted liquid chromatography tandem mass spectrometry (LC-MS/MS) metabolomics analyses. Despite their wide use, little work has been attempted to systematically compare their performance due to the difficulty and cost of performing comparisons with real experimental data due to the lack of ground truth and the costs involved in running large number of acquisitions. Here, we present a systematic in-silico comparison of these two acquisition methods. To do so, we extended our Virtual Metabolomics Mass Spectrometer (ViMMS) framework with a DIA module. Our results show that the performance of these methods varies with the average number of co-eluting ions as the most important factor. At low numbers, DIA outperforms DDA, but at higher numbers, DDA has an advantage as DIA can no longer deal with the large amount of overlapping ion chromatograms. Results from simulation were further validated on an actual mass spectrometer, demonstrating that using ViMMS we can draw conclusions from simulation that translate well into the real world. Embedding this work within ViMMS also allows for researchers to easily simulate DDA and DIA LC-MS/MS runs, validate them on actual instrument, and potentially prototype novel methods that best combine the characteristics of both approaches. We believe that this work provides a useful guide to the choice of DIA or DDA for scientists involved in metabolomics data acquisition.

systems biology↗

bioRxiv2021

GraphOmics: an Interactive Platform to Explore and Integrate Multi-Omics Data

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSBackgroundC_ST_ABSAn increasing number of studies now produce multiple omics measurements that require using sophisticated computational methods for analysis. While each omics data can be examined separately, jointly integrating multiple omics data allows for a deeper understanding and insights to be gained from the study. In particular data integration can be performed horizontally, where biological entities from multiple omics measurements are mapped to common reactions and pathways. However, data integration remains a challenge due to the complexity of the data and the difficulty in interpreting analysis results. ResultsHere we present GraphOmics, a user-friendly platform to explore, integrate multiple omics datasets and support hypothesis generations. Users can upload transcriptomics, proteomics and metabolomics data to GraphOmics. Relevant entities are connected based on their biochemical relationships, and mapped to reactions and pathways from Reactome. From the Data Browser in GraphOmics, mapped entities and pathways can be ranked, sorted and filtered according to their statistical significance (p-values) and fold changes. Context-sensitive panels provide information on the currently selected entities, while interactive heatmaps and clustering functionalities are also available. As a case study, we demonstrated how GraphOmics was used to interactively explore multi-omics data and support hypothesis generations using two complex datasets from existing Zebrafish regeneration and Covid-19 human studies. ConclusionsGraphOmics is fully open-sourced and freely accessible from https://graphomics.glasgowcompbio.org/. It can be used to integrate multiple omics data horizontally by mapping entities across omics to reactions and pathways. Our demonstration showed that using interactive explorations from GraphOmics, interesting insights and biological hypotheses could be rapidly revealed.

bioinformatics↗

bioRxiv2020

Rapid Development of Improved Data-dependent Acquisition Strategies.

Tandem mass spectrometry (LC-MS/MS) is widely used to identify unknown ions in untargeted metabolomics. Data Dependent Acquisition (DDA) chooses which ions to fragment based upon intensity observed in MS1 survey scans and typically only fragment a small subset of the ions present. Despite this inefficiency, relatively little work has addressed the development of new DDA methods, partly due to the high overhead associated with running the many extracts necessary to optimise approaches in busy MS facilities. In this work, we firstly provide theoretical results that show how much improvement is possible over current DDA strategies. We then describe an in silico framework for fast and cost efficient development of new DDA acquisition strategies using a previously developed Virtual Metabolomics Mass Spectrometer (ViMMS). Additional functionality is added to ViMMS to allow methods to be used both in simulation and on real samples via an instrument application programming interface (API). We demonstrate this framework through the development and optimisation of two new DDA methods which introduce new advanced ion prioritisation strategies. Upon application of the here developed methods to two complex metabolite mixtures, our results show that they are able to fragment more unique ions than standard DDA acquisition strategies.

bioinformatics↗

bioRxiv2020

Decomposing metabolite set activity levels with PALS

MotivationRelated metabolites can be grouped into metabolite sets in many ways. Examples of these include the grouping of metabolites through their participation in a series of chemical reactions (forming metabolic pathways); or based on fragmentation spectral similarities and shared chemical substructures. Understanding how such metabolite sets change across samples can be incredibly useful in the interpretation and understanding of complex metabolomics data. However many of the available tools suitable for the enrichment analysis of metabolite sets are based on simple methods that badly handle the missing features inherent in untargeted metabolomics measurements and can be difficult to integrate into existing applications. ResultsWe present PALS (Pathway Activity Level Scoring), a Python library, command-line tool and Web application that performs the ranking of significantly-changing metabolite sets over different experimental conditions. As example applications, PALS is used to analyse metabolites grouped as pathways and by common MS-MS fragmentation structures. A comparison of PALS with two other commonly used methods (ORA and GSEA) is also given, and reveals that PALS is more robust to missing peaks and noisy data than the alternatives. We report results from using PALS to analyse pathways from a study of Human African Trypanosomiasis. Finally, we also report how PALS used tandem MS fragmentation structures to reveal enriched metabolite sets between clades in Rhamnaceae plant data, and on American Gut Project data. AvailabilityPALS is freely available from our project Web site at https://pals.glasgowcompbio.org/. It can be imported as a Python library, run as a stand-alone tool or used as a web application.

bioinformatics↗

bioRxiv2019

In-silico Optimisation of Mass Spectrometry Fragmentation Strategies in Metabolomics

Liquid-Chromatography (LC) coupled to tandem mass spectrometry (MS/MS) is widely used in identifying small molecules in untargeted metabolomics. Various strategies exist to acquire MS/MS fragmentation spectra; however, the development of new acquisition strategies is hampered by the lack of simulators that let researchers prototype, compare, and optimise strategies before validations on real machines. We introduce Virtual Metabolomics Mass Spectrometer (ViMMS), a modular metabolomics LC-MS/MS simulator framework that allows for scan-level control of the MS2 acquisition process in-silico. ViMMS can generate new LC-MS/MS data based on empirical data or virtually re-run a previous LC-MS/MS analysis using pre-existing data in-silico to allow the testing of different fragmentation strategies. It allows the comparison of different fragmentation strategies on real data, with the resulting scan results extractable as mzML files. To demonstrate its utility, we show how our proposed framework can be used to take the output of a real tandem mass spectrometry analysis and examine the effect of varying parameters in Top-N Data Dependent Acquisition protocol. We also demonstrate how ViMMS can be used to compare a recently published Data-set-Dependent Acquisition strategy with a standard Top-N strategy. We expect that ViMMS will save method development time by allowing for offline evaluation of novel fragmentation strategies and optimisation of fragmentation strategy for a particular experiment.

bioinformatics↗

bioRxiv2019

MolNetEnhancer: enhanced molecular networks by integrating metabolome mining and annotation tools

Metabolomics has started to embrace computational approaches for chemical interpretation of large data sets. Yet, metabolite annotation remains a key challenge. Recently, molecular networking and MS2LDA emerged as molecular mining tools that find molecular families and substructures in mass spectrometry fragmentation data. Moreover, in silico annotation tools obtain and rank candidate molecules for fragmentation spectra. Ideally, all structural information obtained and inferred from these computational tools could be combined to increase the resulting chemical insight one can obtain from a data set. However, integration is currently hampered as each tool has its own output format and efficient matching of data across these tools is lacking. Here, we introduce MolNetEnhancer, a workflow that combines the outputs from molecular networking, MS2LDA, in silico annotation tools (such as Network Annotation Propagation or DEREPLICATOR) and the automated chemical classification through ClassyFire to provide a more comprehensive chemical overview of metabolomics data whilst at the same time illuminating structural details for each fragmentation spectrum. We present examples from four plant and bacterial case studies and show how MolNetEnhancer enables the chemical annotation, visualization, and discovery of the subtle substructural diversity within molecular families. We conclude that MolNetEnhancer is a useful tool that greatly assists the metabolomics researcher in deciphering the metabolome through combination of multiple independent in silico pipelines.

biochemistry↗

bioRxiv2018

Deciphering complex metabolite mixtures by unsupervised and supervised substructure discovery and semi-automated annotation from MS/MS spectra

O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=94 SRC=\"FIGDIR/small/491506_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (19K):\norg.highwire.dtl.DTLVardef@1382d8borg.highwire.dtl.DTLVardef@13636a4org.highwire.dtl.DTLVardef@3d7decorg.highwire.dtl.DTLVardef@1161d62_HPS_FORMAT_FIGEXP M_FIG Integration of MS2LDA substructure discovery with MAGMa spectral annotations and ClassyFire term predictions complemented with MotifDB significantly advance metabolite annotation.\n\nC_FIG Complex metabolite mixtures are challenging to unravel. Mass spectrometry (MS) is a widely used and sensitive technique to obtain structural information on complex mixtures. However, just knowing the molecular masses of the mixtures constituents is almost always insufficient for confident assignment of the associated chemical structures. Structural information can be augmented through MS fragmentation experiments whereby detected metabolites are fragmented giving rise to MS/MS spectra. However, how can we maximize the structural information we gain from fragmentation spectra?\n\nWe recently proposed a substructure-based strategy to enhance metabolite annotation for complex mixtures by considering metabolites as the sum of (bio)chemically relevant moieties that we can detect through mass spectrometry fragmentation approaches. Our MS2LDA tool allows us to discover - unsupervised - groups of mass fragments and/or neutral losses termed Mass2Motifs that often correspond to substructures. After manual annotation, these Mass2Motifs can be used in subsequent MS2LDA analyses of new datasets, thereby providing structural annotations for many molecules that are not present in spectral databases.\n\nHere, we describe how additional strategies, taking advantage of i) combinatorial in-silico matching of experimental mass features to substructures of candidate molecules, and ii) automated machine learning classification of molecules, can facilitate semi-automated annotation of substructures. We show how our approach accelerates the Mass2Motif annotation process and therefore broadens the chemical space spanned by characterized motifs. Our machine learning model used to classify fragmentation spectra learns the relationships between fragment spectra and chemical features. Classification prediction on these features can be aggregated for all molecules that contribute to a particular Mass2Motif and guide Mass2Motif annotations.\n\nTo make annotated Mass2Motifs available to the community, we also present motifDB: an open database of Mass2Motifs that can be browsed and accessed programmatically through an API. MotifDB is integrated within ms2lda.org, allowing users to efficiently search for characterized motifs in their own experiments. We expect that with an increasing number of Mass2Motif annotations available through a growing database we can more quickly gain insight in the constituents of complex mixtures. That will allow prioritization towards novel or unexpected chemistries and faster recognition of known biochemical building blocks.

bioinformatics↗