Biology subjects

van der Hooft, J. J. J.

Publications and source records attributed to van der Hooft, J. J. J..

At least 19 recordsLinked to original sources

bioRxiv2026

The Evolving Bileome: A Pan-Repository Landscape of Expanded Bile Acid and Related Steroidal Signatures

Bile acids and related steroidal metabolites are among the most studied biomolecules, yet recent work has revealed far greater structural diversity than previously recognized, much of it shaped by the microbiome. We previously introduced a repository-scale bile acid MS/MS library built from queries targeting C24 bile acid fragmentation, limited to Orbitrap Q Exactive datasets within GNPS/MassIVE. Here we announce a major expansion of this library: a bile acid and steroidal MS/MS spectral library assembled across three major metabolomics repositories, now capturing C23, C24 (including alkamine), C26 (C-C bonded aceto derivatives), and C27 bile acids, as well as C28 bile alcohols. The expanded libraries comprise 640,141 unique MS/MS spectra, of which 120,293 (18.8%) match 2,529 reference spectra, leaving most of this steroidal chemical space structurally unassigned. Linked to harmonized pan-repository metadata, the library provides a starting point for classifying MS/MS spectra as likely bile acids or related steroids, with two example analyses illustrated here. This resource allows researchers to flag candidate bile acid and steroidal signatures in their own data for further characterization.

bioinformatics↗

bioRxiv2026

Beyond the Default: Optimizing Molecular Networking with arteMIS

Metabolomics uses tandem mass spectrometry (MS/MS) data to gain structural insights of small molecules that play biological roles, generating datasets whose size and complexity demand systematic organisation. Molecular networking addresses this by representing MS/MS spectra as nodes and their pairwise similarity as edges, but its output is critically sensitive to user-defined parameters: similarity score cut-off, maximum component size, maximum links and minimum matching peaks. These parameters are routinely left at default values, which can either collapse interpretable molecular families into entangled "hairballs" or fragment them into disconnected singletons. In the absence of ground truth, no standardised framework exists to evaluate molecular networks or to assess whether their connections are robust to run-to-run variability present in metabolomic experiments. Here, we introduce arteMIS (Accelerated Ranking and Tuning using Multi-metric Interpretability across Scores), a framework for systematic parameter optimisation that uses Latin Hypercube Sampling to efficiently cover the four-dimensional parameter space and ranks candidate networks through a user-tuneable composite Z-score, combining topology- and chemistry-based metrics. This framework supports three complementary modes: global, seed, and target-class, adapting optimisation to fully unannotated datasets, curated subset of reference features or class-focused discovery, respectively. Benchmarking across four spectral libraries (~600 to ~13,000 spectra) and four scoring methods (Cosine, Modified Cosine, Spec2Vec, MS2DeepScore), we provide practical guidance for parameter selection as a function of scoring method and dataset size and show that optimal settings do not transfer between them. Top-ranked arteMIS configurations outperformed GNPS defaults in chemistry and topology metrics and produced networks with higher edge-stability under subsampling. Applied to actinobacteria and fungal samples, arteMIS rescued structurally meaningful families that remained fragmented under default settings. We conclude that arteMIS reframes molecular network construction from a default-driven step into a task-customisable optimisation.

bioinformatics↗

bioRxiv2026

Quantifying per-match Reliability in Library Matching for Untargeted Metabolomics Workflows

Tandem mass spectrometry has become central to untargeted metabolomics. The translation of unknown spectra into biological insight depends on assigning chemical identities to detected metabolites. Structural characterization typically begins with mass spectral library matching, in which experimental spectra are compared against reference libraries and candidate annotations are ranked by their spectral similarity to the query. As spectral libraries and experimental datasets grow, however, more candidates achieve comparable similarity scores for a single query, and similarity scores give no indication of how reproducible a candidate match is or how sensitive it is to the underlying fragment evidence. Existing false-discovery-rate approaches can indicate annotation error at the dataset level but do not provide a per-match estimate of reliability. Here, we introduce a SpecReBoot-inspired query-focused bootstrapping approach that resamples the fragment evidence of each query spectrum. This approach relies on recomputing query similarity to candidate library spectra across bootstrap replicates, which provides a statistical distribution of scores rather than a single value. From this distribution we define the match support, a per-match reliability estimate quantifying the reproducibility of a match under spectral perturbation, together with measures of ranking stability that describe how often a candidate remains among the top-ranked matches across replicates. Applied to a forensic drug-of-abuse case, match support distinguished previously identified annotations from high-scoring false positives: a distinction cosine similarity failed to make. Furthermore, match support values remained stable as the reference library was expanded, whereas ranking stability metrics shifted significantly. In a cross-instrument endogenous metabolite library search, match support further revealed metric-specific annotation behavior, identifying metabolites consistently supported across different similarity metrics, while flagging annotations whose reliability depended strongly on the chosen scoring metric. Benchmarking against a natural-product reference library demonstrated that ranking based on match support values promoted true matches by four ranks on average compared with cosine-based ranking, without promoting analogs. Under controlled spectral perturbation experiments, match support flagged incorrect annotations with an AUROC of 0.75, whereas the cosine similarity score alone of the same match reached only 0.56. Query-focused bootstrapping thus provides a practical, per-match measure of annotation reliability, bringing the field a step toward reliable annotations at scale. We anticipate that incorporation of our annotation reliability scoring into computational metabolomics workflows will further promote the growth of spectral libraries and enhance their applicability across scientific disciplines.

biochemistry↗

bioRxiv2026

PlantMetWiki: a FAIR knowledge graph for plant metabolic pathway cross-species representation and integration

Plants produce a vast diversity of specialized metabolites with extensive potential ecological, agro-industrial, and pharmaceutical applications. Discovery of novel plant natural products relies on combining multi-omics evidence with biochemical transformations. However, pathway-level annotations are fragmented across individual species, databases, and publications, limiting comparative cross-species pathway analyses and systematic generation of hypotheses. To support integration and reuse of plant metabolic knowledge, we developed PlantMetWiki, a FAIR Linked Open Data semantically enriched knowledge graph built on infrastructure adapted from WikiPathways. Our approach extends on the highly curated pathway information from Plant Metabolic Network with crosslinks to biosynthetic gene clusters resources (MIBiG and plantiSMASH), metabolite annotations, and cross-species modelling. This way, our resource captures pathway genomic context, increases metabolomics data interoperability via federated queries, and supports cross-species analysis to identify annotation gaps. PlantMetWiki represents 1,162 plant metabolic pathways as Resource Description Framework (RDF) graphs, preserving pathway structure, literature provenance, annotations, and taxonomic information from PlantCyc 17.0. PlantMetWiki is distributed through a public SPARQL endpoint with open-source reproducible data transformation and validation workflows. By modelling pathways as a multispecies graph, PlantMetWiki enables comparative analyses across taxa, integration with external chemical knowledge resources through federated queries, and identification of metabolic, genomic, and chemical annotation gaps. As a result, PlantMetWiki provides a foundation for FAIR reuse and integration of plant pathway knowledge.

bioinformatics↗

bioRxiv2026

RetroMol: Parsing a shared encoding from natural products and their biosynthetic gene clusters

Natural products such as polyketides and nonribosomal peptides (NRPs) are important sources of bioactive compounds, including many antibiotics. Many of them are assembled by modular enzyme complexes and further modified and diversified by tailoring reactions encoded by biosynthetic gene clusters (BGCs). Although natural products and their coding BGCs describe different data modalities of the same biochemical process, a unified language to jointly describe their biochemistry is lacking. Here we introduce a sequence-based representation of the core biosynthesis of modular natural products, which we call primary sequences, that bridges chemical structures and BGCs. We also present RetroMol, an algorithm that parses either natural product structures or their encoding BGCs into their primary sequences of natural product building blocks. RetroMol allows for similarity scoring between natural products and BGCs, enabling the retrieval of compounds, BGCs, and a combination of the two, based on their biosynthetic similarity. This can, for instance, be used to retrieve biosynthetically similar but structurally dissimilar compounds, or link natural products to candidate coding BGCs in large experimental datasets. We demonstrate the latter by rediscovering the nocardichelin B BGC as a proof of principle. We also exemplify the utility of biosynthetic similarity by showing various pairs of biosynthetically similar compounds with low structural similarity. Together, these results establish primary sequences as a shared biosynthetic encoding for natural product comparison and BGC prioritization.

bioinformatics↗

bioRxiv2026

Characterizing the effect of short wavelengths on the floral flavonoid metabolome of medicinal cannabis using a comparative computational metabolomics workflow

BackgroundControlled-environment cultivation of medicinal cannabis (Cannabis sativa L.) typically optimizes light conditions to enhance the biosynthesis of pharmaceutically important metabolites like cannabinoids. Such experimental strategies may also influence other specialized metabolites like terpenoids, flavonoids, alkaloids, among others. Previous untargeted metabolomics studies testing short wavelength conditions like UV and blue light have shown that terpenoids and prenylated flavonoids in cannabis leaves respond differentially. However, since metabolomic studies in cannabis have so far mostly focused on floral cannabinoids, a comprehensive untargeted study into cannabis floral metabolome response to short wavelengths is currently lacking. ObjectivesOur study investigates the impact of short wavelength usage on cannabis specialized metabolism, and in particular the influence of UVB, UVA, and blue light on the cannabis floral flavonoid metabolome and associated glycosylation moieties. MethodsCannabis plants were grown under a white background light and exposed to supplemental UVB, UVA, or blue light during the generative phase of the cultivation cycle. Treatments were compared to a reference white background light without UV or blue light. Metabolites from floral tissue were extracted and analyzed via ultra-performance liquid chromatography-tandem mass spectrometry. A comparative metabolomics workflow was designed and used to characterize the floral flavonoid metabolome and associated glycosylation moieties. ResultsOur results demonstrate how short wavelengths differentially affect the metabolism of natural product compound classes including polyketides and phenylpropanoids/shikimates. Blue light induced flavonoids similarly to how UVB did, while both UVA and blue light specifically induced flavanones accumulation. UVB showed the strongest regulatory effect on flavonoids production and glycosylation patterns. ConclusionsUVB reshapes the cannabis floral flavonoid metabolome by selectively stimulating the accumulation and structural modification of flavonoids. Therefore, UVB application in cannabis cultivation represents a useful horticultural strategy to increase inflorescence medicinal quality without affecting cannabinoid levels.

biochemistry↗

bioRxiv2026

Systematic mass-spectrometry-guided metabolic fingerprinting elucidates diversity of specialized metabolites across the Brassicaceae

O_LIPlants produce diverse bouquets of specialized metabolites (SMs), yet only a fraction of the vast phytochemical space has been explored to date. Comparative analysis of SM profiles can reveal hotspots of biochemical novelty, while systematic profiling across taxonomic levels does presently not cover large plant families. C_LIO_LITo study core and accessory SM profiles in the Brassicaceae plant family, we fingerprinted 14 species by Liquid-Chromatography Mass-Spectrometry (LCMS/MS). We develop standardized experimental and computational workflows integrating in silico annotation tools to study consensus compound class and substructure distributions of SMs. Furthermore, we investigate the congruence of chemotaxonomy and species phylogeny across an extended panel of 17 species. C_LIO_LIUnique metabolite profiles were outstanding in Camelina sativa, Capsella rubella, and B. vulgaris, with the largest unique terpenoid profile annotated in C. sativa, accounting for 33.5% and 55.6% in positive and negative ionization mode, respectively. Substructure motifs were found to overlap with compound class predictions, highlighted for triterpenoids in Camelinodae. Furthermore, dual-tissue chemotaxonomic clustering resembled relationships of Brassica subgenomes across tissues. C_LIO_LIWe anticipate that our systematic approach can serve as a blueprint for investigating biochemical diversity in other plant lineages and can boost the characterization of plant natural product pathways. C_LI

plant biology↗

bioRxiv2026

Metabolic fingerprinting of 17 Brassicaceae species across three tissues

Plants produce the most diverse blends of specialized metabolites on earth. Natural products derived from plants are valuable resources for drug development, food chemistry, and crop resistance breeding. Phenotypes of specialized metabolite profiles can be captured by untargeted mass-spectrometry across species phylogeny, tissues, and genotypes. Here, we collected metabolic fingerprints of 17 Brassicaceae species across three tissues (paired leaf and root; flower) using liquid chromatography-tandem mass spectrometry (LC-MS/MS) in positive and negative ionization mode. Corresponding metadata has been refined for reuse according to ReDU guidelines, and for integration with public genomic and transcriptomic data. Standardization of in vitro growth conditions, and data processing workflows enables integration of acquired raw and processed data across platforms for single- and multi-omics analysis. Further, the inclusion of tissue-specific metabolic profiles across ploidy levels, as well as across crop species and wild relatives, makes this dataset a valuable resource for natural product discovery.

plant biology↗

bioRxiv2026

Pathogen-induced red pigmentation uncovers a conserved floral defense in Asteraceae

Flower colour is predominantly selected for visual appeal and pollinator-driven evolution, however its role in plant-pathogen interaction remains unclear. Here, we investigated the relationship between anthocyanin accumulation and resistance to the necrotrophic pathogen Botrytis cinerea in Chrysanthemum morifolium. Using a unique set of spontaneous colour mutants sharing a common genetic background, we demonstrate that higher anthocyanin levels are associated with enhanced resistance to B. cinerea. Furthermore, we also observed pathogen induced anthocyanin accumulation at pathogen penetration sites in the white cultivars. Transcriptomics and metabolomics analyses revealed shift in metabolic pathways from flavonols towards flavone and anthocyanin biosynthesis during infections. A broader survey of taxonomically distant flowering species identified pathogen-induced red pigmentation is largely restricted to the members of the Asteraceae family. Notably, we observed divergence between wild and domesticated species, where the wild species retained the inducible pigmentation response and exhibited resistant to B. cinerea, whereas the cultivated species showed loss of this response, correlating with its increased susceptibility. Overall, our findings indicate that breeding for ornamental traits may have inadvertently selected against floral immunity.

plant biology↗

bioRxiv2026

Bootstrap resampling of mass spectral pairs with SpecReBoot reveals hidden molecular relationships

Mass spectral molecular networking organizes tandem mass spectrometry (MS/MS) data by connecting spectra based on similarity scores. However, these deterministic metrics provide no measure of uncertainty, so molecular networks often retain edges arising from noise or missing fragments, while authentic chemical relationships may remain obscured. To address this gap, we present SpecReBoot, a statistical framework that adapts Felsensteins bootstrap principle to MS/MS similarity scoring. By resampling fragment-level features and recomputing similarity across replicates, SpecReBoot transforms a single score into a confidence distribution and assigns bootstrap support to network edges. Using large, curated MS/MS spectral datasets, we demonstrate that SpecReBoot systematically removes unreliable connections and recovers robust low-similarity links, refining networks and revealing hidden relationships. We highlight how this confidence-aware "rebooting" guided the discovery of an unprecedented macrolactone scaffold from the endophytic fungus Diaporthe caliensis. Altogether, SpecReBoot provides the first general framework for quantifying confidence in MS/MS similarity and molecular network analysis.

biochemistry↗

bioRxiv2025

When flowers turn red: Pathogen induced flavonoid and anthocyanin biosynthesis in Chrysanthemum seticuspe confers resistanceto Botrytis cinerea

Botrytis cinerea is a major threat to ornamental crops, yet floral defence responses remain poorly understood. In Chrysanthemum seticuspe, we found that flower petals, unlike leaves, mount a localized resistance response resulting in red spots appearing at fungal penetration sites. We observed an intensification of the coloured response as infections progressed. To investigate the basis of this phenotype, we performed a time-course paired transcriptomic and metabolomic analysis on mock-inoculated vs B. cinerea-inoculated petals and leaves. Infection triggered strong transcriptional reprogramming in petals, with clear induction of phenylpropanoid and flavonoid/anthocyanin pathway genes and candidate regulators, consistent with the visible pigmentation. Metabolite profiles reflected this response, showing time-dependent accumulation of infection induced flavonoids such as quercetin, tilianin, and their derivatives, as well as cyanidin-based anthocyanins in infected petals. Integrating both omics datasets with MEANtools highlighted an anthocyanin-associated transcript-metabolite module, including a module putatively involved in the synthesis of polyyne-type phytoalexins. Antifungal assays demonstrated that selected flavonoids and cyanidin derivatives inhibit B. cinerea in a dose-dependent manner, supporting a direct antifungal role of these compounds. Altogether, our results show that C. seticuspe petals deploy a spatially confined, multi-layered chemical defence in which pathogen-induced flavonoids and anthocyanins operate as active components of resistance against a necrotrophic pathogen. We anticipate that future paired omics analyses in combination with spatial omics and bioactivity assays will yield insights into the role of specialised defence molecules in response to biotic and abiotic stresses. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=75 SRC="FIGDIR/small/695701v1_ufig1.gif" ALT="Figure 1"> View larger version (29K): org.highwire.dtl.DTLVardef@e5dbeborg.highwire.dtl.DTLVardef@c94bd1org.highwire.dtl.DTLVardef@190c030org.highwire.dtl.DTLVardef@39a7b5_HPS_FORMAT_FIGEXP M_FIG C_FIG

plant biology↗

bioRxiv2025

Fine-Grained Structural Classification of Biosynthetic Gene Cluster-Encoded Products

Biosynthetic gene clusters (BGCs) are responsible the biosynthesis of many natural products, including a multitude of effective therapeutics and their precursors. Advances in genomic data collection as well as computational techniques have made it possible to identify BGCs at scale. However, accurately determining the types of BGC-encoded products from genomic content remains elusive. Here, we introduce BGCat (BGC annotation tool), a machine learning method for fine-grained structural classification of BGC-encoded products, leveraging the NPClassifier natural product nomenclature. Our method leverages a pre-trained protein language model for creating meaningful gene representations and a deep neural network for class label prediction. We show the method outperforms state-of-the-art approaches in coarse-grained product classification and is effective for detailed classification. We implement a clustering-based augmentation strategy for BGC-product relationships, addressing a crucial gap in the available datasets. We then introduce the concept of product class profiles (PCPs) of gene cluster families (GCFs), associating each GCF with a probabilisitc distribution of product types and offering a new perspective on GCF functions. Lastly, we use BGCat to provide new product class labels for over 100k BGCs in antiSMASH DB that presently have minimal information about their products.

bioinformatics↗

bioRxiv2025

plantiSMASH 2.0: improvements to detection, annotation, and prioritization of plant biosynthetic gene clusters

Plants produce bioactive compounds as part of their specialized metabolism, with applications in medicine, agriculture, and nutrition. The biosynthesis of a growing number of these specialized metabolites has been found to be encoded in biosynthetic gene clusters (BGCs), creating increasing demand for genome mining tools to automate their detection. plantiSMASH enables the identification of putative plant BGCs through a rule-based approach, available via both command-line and web interfaces. Here, we present plantiSMASH 2.0 (https://plantismash.bioinformatics.nl/), a major update that expands and improves the original framework with revised and additional BGC detection rules (now supporting 12 BGC types), substrate prediction for selected enzyme families, and regulatory analysis through transcription factor binding site detection. The updated plantiSMASH 2.0 database includes 30,423 putative BGCs across 430 genomes. Together, these improvements make plantiSMASH 2.0 a powerful and comprehensive platform for the detection and characterization of plant biosynthetic pathways, supporting and accelerating research in plant specialized metabolism and plant natural product discovery.

bioinformatics↗

bioRxiv2025

Using cross-species co-expression to predict metabolic interactions in microbiomes.

In microbial ecosystems, metabolic interactions are key determinants of species relative abundance and activity. Given the immense number of possible interactions in microbial communities, their experimental characterization is best guided by testable hypotheses generated through computational predictions. However, widely adopted software tools - such as those utilizing microbial co-occurrence - typically fail to highlight the pathways underlying these interactions. Bridging this gap will require methods that utilize microbial activity data to infer putative target pathways for experimental validation. In this study, we explored a novel approach by applying cross-species co-expression to predict interactions from microbial co-culture RNA-sequencing data. Specifically, we investigated the extent to which co-expression between genes and pathways of different bacterial species can predict competition, cross-feeding, and specialized metabolic interactions. Our analysis of the Mucin and Diet-based Minimal Microbiome (MDb-MM) data yielded results consistent with previous findings and demonstrated the methods potential to identify pathways that are subject to resource competition. Our analysis of the Hitchhikers of the Rhizosphere (THOR) data showed links between related specialized functions, for instance, between antibiotic and multidrug efflux system expression. Additionally, siderophore co-expression and further evidence suggested that increased siderophore production of the Pseudomonas koreensis koreenceine BGC deletion-mutant drives siderophore production in the other community members. In summary, our findings confirm the feasibility of using cross-species co-expression to predict pathways potentially involved in microbe-microbe interactions. We anticipate that the approach will also facilitate the discovery of novel gene functions through their association with other species metabolic pathways, for example, those involved in antibiotic response. ImportanceAn improved mechanistic understanding of microbial interactions can guide targeted interventions or inform the rational design of microbial communities to optimize them for applications such as pathogen control, food fermentation, and various biochemical processes. Existing methodologies for inferring the mechanisms behind microbial interactions often rely on complex model-building and are therefore sensitive to the introduction of biases from the incorporated existing knowledge and model-building assumptions. We highlight the microbial interaction prediction potential of cross-species co-expression analysis, which contrasts with these methods by its data-driven nature. We describe the utility of cross-species co-expression for various types of interactions and thereby inform future studies on use-cases of the approach and the opportunities and pitfalls that can be expected in its application.

bioinformatics↗

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

Chemistry-based vectors map the chemical space of natural biomes from untargeted mass spectrometry data

Untargeted metabolomics can comprehensively map the chemical space of a biome, but is limited by low annotation rates (<10%). We used chemistry-based vectors, consisting of molecular fingerprints or chemical compound classes, predicted from mass spectrometry data, to characterize compounds and samples. These chemical characteristics vectors (CCVs) estimate the fraction of compounds with specific chemical properties in a sample. Unlike the aligned MS1 data with intensity information, CCVs incorporate actual chemical properties of compounds, offering deeper insights into sample comparisons. Thus, we identified key compound classes differentiating biomes, such as ethers which are enriched in environmental biomes, while steroids enriched in animal host-related biomes. In biomes with greater variability, CCVs revealed key clustering compound classes, such as organonitrogen compounds in animal distal gut and lipids in animal secretions. CCVs thus enhance the interpretation of untargeted metabolomic data, providing a quantifiable and generalizable understanding of the chemical space of natural biomes.

bioinformatics↗

bioRxiv2024

MEANtools: multi-omics integration towards metabolite anticipation and biosynthetic pathway prediction

During evolution, plants have developed the ability to produce a vast array of specialized metabolites, which play crucial roles in helping plants adapt to different environmental niches. However, their biosynthetic pathways remain largely elusive. In the past decades, increasing numbers of plant biosynthetic pathways have been elucidated based on approaches utilizing genomics, transcriptomics, and metabolomics. These efforts, however, are limited by the fact that they typically adopt a target-based approach, requiring prior knowledge. Here, we present MEANtools, a systematic and unsupervised computational integrative omics workflow to predict candidate metabolic pathways de novo by leveraging knowledge of general reaction rules and metabolic structures stored in public databases. In our approach, possible connections between metabolites and transcripts that show correlated abundance across samples are identified using reaction rules linked to the transcript-encoded enzyme families. MEANtools thus assesses whether these reactions can connect transcript-correlated mass features within a candidate metabolic pathway. We validate MEANtools using a paired transcriptomic-metabolomic dataset recently generated to reconstruct the falcarindiol biosynthetic pathway in tomato. MEANtools correctly anticipated five out of seven steps of the characterized pathway and also identified other candidate pathways involved in specialized metabolism, which demonstrates its potential for hypothesis generation. Altogether, MEANtools represents a significant advancement to integrate multi-omics data for the elucidation of biochemical pathways in plants and beyond.

bioinformatics↗

bioRxiv2024

Reliable cross-ion mode chemical similarity prediction between MS2 spectra

Mass spectrometry is a cornerstone of untargeted metabolomics, enabling the characterization of metabolites in both positive and negative ionization modes. However, comparisons across ionization modes have remained a substantial challenge due to the distinct fragmentation patterns produced by each polarity. To overcome this barrier, we present MS2DeepScore 2.0, a machine learning-based model to predict chemical similarity between mass fragmentation spectra, which works both between different and the same ionization modes. We demonstrate the utility of MS2DeepScore 2.0 in three case studies, where MS2DeepScore enabled cross-ionization mode molecular networking, enhancing data exploration and metabolite annotation. To ensure robustness, we have implemented a quality estimation method that flags spectra with low information content or those dissimilar to the training data, thereby minimizing false predictions. Altogether, MS2DeepScore 2.0 extends our current capabilities in organizing, exploring, and annotating untargeted metabolomics profiles.

bioinformatics↗