Justin J. J. van der Hooft

The paper expands a bile acid and steroidal MS/MS spectral library across three repositories, capturing diverse structures and providing a resource for classifying unknown spectra.

The paper uses mass-spectrometry to systematically profile specialized metabolites across Brassicaceae species, identifying unique profiles and substructure motifs.

This paper uses metabolomics co-variation networks to analyze urobiome-pathogen interactions, identifying keystone species and emergent pathogen states in a synthetic urobiome model.

The paper introduces arteMIS, a framework for optimizing molecular network parameters in metabolomics, showing improved network quality over default settings.

The paper introduces BGCat, a machine learning tool for fine-grained classification of biosynthetic gene cluster-encoded products, improving upon existing methods and labeling over 100k BGCs.

This paper introduces a method to assess the reliability of metabolite annotations in untargeted metabolomics by evaluating match reproducibility under spectral perturbation.

PlantMetWiki integrates plant metabolic pathway data across species to enable cross-species analysis and hypothesis generation.

The paper improves MS2LDA for substructure discovery in mass spectrometry by enhancing speed and introducing automated annotation guidance, validated on chemical datasets.

This study examines how short wavelengths like UVB, UVA, and blue light affect the floral flavonoid metabolome of cannabis using a comparative metabolomics approach.

RetroMol converts natural product structures and biosynthetic gene clusters into shared primary sequences to enable biosynthetic similarity comparisons.

The paper collects and standardizes metabolic fingerprints of 17 Brassicaceae species across three tissues using LC-MS/MS, enabling integration with genomic and transcriptomic data for natural product discovery.

SpecReBoot improves molecular network analysis by quantifying confidence in MS/MS similarity scores, revealing hidden chemical relationships.

plantiSMASH 2.0 improves detection, annotation, and prioritization of plant biosynthetic gene clusters by expanding BGC types and adding substrate prediction and regulatory analysis.

The paper shows that higher anthocyanin levels in flowers correlate with increased resistance to Botrytis cinerea, and that this defense is conserved in Asteraceae but lost in cultivated species.

This study identifies pathogen-induced flavonoid and anthocyanin biosynthesis in Chrysanthemum seticuspe petals as a defense mechanism against Botrytis cinerea, showing their direct antifungal activity.

This paper predicts microbial metabolic interactions using cross-species co-expression analysis of RNA-sequencing data from microbiome datasets.

MEANtools predicts metabolic pathways de novo by integrating transcriptomic and metabolomic data using reaction rules and enzyme families.

The paper assesses how olive mill solid waste affects the specialized metabolite profiles of two mushrooms using computational metabolomics.

The paper uses chemistry-based vectors to analyze untargeted mass spectrometry data and identifies compound classes that differentiate natural biomes.

The paper introduces the Molecules Gateway, a database of 150,000 annotated molecules from Actinomycetes, enabling rapid identification of microbial products and analysis of chemical diversity.

This study uses computational metabolomics to map the chemical diversity of Cannabis beyond cannabinoids, revealing distinct profiles in two cultivars and their potential medicinal properties.

The paper curates LC-MS/MS data from plant extracts to create a taxonomically focused database for exploring plant-derived molecules.

The paper introduces specXplore, an interactive dashboard for exploring mass spectral data, enabling structural hypothesis generation through visualization and spectral similarity analysis.

MS2DeepScore 2.0 predicts chemical similarity between tandem mass spectra across ionization modes, improving metabolite annotation and data exploration in untargeted metabolomics.

The paper creates a suspect spectral library for untargeted metabolomics by propagating annotations from public MS/MS spectra to improve interpretation of unannotated spectra.

iPRESTO detects sub-clusters in biosynthetic gene clusters to link natural product substructures to their genetic origins, improving annotation and discovery.

This study benchmarks DDA and DIA acquisition methods in metabolomics using simulations and real-world validation, showing performance differences based on co-eluting ion counts.

FERMO is a dashboard that automates the prioritization of molecular features from mass spectral data to link molecules to phenotypes.

NPOmix connects mass spectrometry fragmentation data to biosynthetic gene clusters using a k-nearest neighbor classifier, improving metabolite discovery and structure prediction.

The paper introduces homologueDiscoverer, an R package for detecting and managing homologue series in LC-MS data through interactive plots and local databases.