Pieter C. Dorrestein

The paper compares cosine, modified cosine, and neutral loss methods for MS/MS spectrum alignment to find structurally related molecules, finding modified cosine performs best.

This study uses native metabolomics to identify a family of protease inhibitors from cyanobacteria, revealing their structure and potent biological activity.

MassQL, an open-source query language, lets users search raw mass spectrometry data from any vendor for isotope, fragmentation and neutral-loss patterns, from one file to entire public repositories.

The paper uses a random forest classifier to predict which drugs are detectable on skin using metabolomics, finding that nervous system drugs are more likely to be detected.

COSMIC assigns confidence to structural annotations from mass spectra by combining database generation, in silico annotation, and a confidence score, outperforming existing methods and annotating novel structures.

The paper introduces falcon, a tool for efficient clustering of large-scale mass spectrometry data, which produces larger and purer clusters than existing tools.

The paper introduces Ion Identity Molecular Networking to connect ion species of the same molecule, improving network connectivity and annotation.

The paper introduces a web-based tool for collaborative analysis of mass spectrometry data, enabling remote inspection, visualization, and analysis of private and public datasets.

EMPress provides an interactive tool for visualizing phylogenetic trees alongside multi-omic data, enabling exploratory analyses of microbiome and metabolome datasets.

CANOPUS is a tool that predicts compound classes from fragmentation spectra, enabling annotation of unknown compounds without reference data.

The paper maps chemical modifications in an ant fungus garden as plant material passes through, revealing its role in nutrient uptake.

The paper introduces Qemistree, a method for analyzing metabolomics mass spectrometry data using hierarchical molecular fingerprints and chemical ontologies to explore chemical composition.

This paper develops genetic tools for heterologous expression of biosynthetic gene clusters in cyanobacteria, enabling high-titer production of cryptomaldamide in Anabaena.

The paper develops a machine learning workflow for auto-deconvolution of GC-MS data within GNPS, enabling molecular networking and applications in breathomics and volatilome mapping.

ZODIAC improves molecular formula annotation in metabolomics by combining SIRIUS results with Gibbs sampling, reducing errors and discovering novel compounds.

The paper introduces FBMN, a method for improving molecular networking by enhancing quantitative analysis and isomer distinction using feature detection and alignment tools in the GNPS platform.

The paper develops ReDU, a tool for discovering and re-analyzing metabolomics data in GNPS, following FAIR principles.

This paper expands the interpretability of human metabolomes by using food-based annotations, increasing the fraction of molecular features understood 7-fold.

A web service renders tandem mass spectra held in metabolomics repositories as publication-ready figures and consistent exports, addressed through Universal Spectrum Identifiers extended to metabolomics.

The paper uses metabolomics to correct metadata classifications in cheetah microbiome studies, revealing antibiotic transfer via grooming and improving understanding of medication effects.

The paper introduces CycloNovo, a de novo cyclopeptide sequencing algorithm, revealing a vast hidden cyclopeptidome in the human gut and other environments.

The paper compares correlation/proportionality methods with MMvec for identifying microbe-metabolite interactions and finds MMvec performs better on real data due to its handling of sparsity.

The paper describes a workflow for creating MS/MS reference spectra in the GNPS infrastructure, achieving 99.2% accurate annotation of chemical standards.

This study shows that Escherichia coli can adapt to use fluorinated indoles as essential precursors for protein synthesis through few genetic mutations and significant regulatory and cellular changes.

The paper identifies novel conjugated bile acids produced by the microbiome in mice and humans using mass spectrometry and a murine 3D model.

MASST enables querying of public small-molecule tandem MS data to discover molecular relationships in metabolomics and natural product datasets.

The paper provides efficient implementations of the Chemical Structural and Compositional Similarity metric in R and Python, significantly improving speed and usability for metabolomics analysis.

This paper uses untargeted mass spectrometry to analyze chemical changes in foods during preparation and storage, finding that some processes alter molecular composition while others primarily involve extraction.

The study examines how geographic separation influenced chemical diversity in Euphorbia, finding reduced diterpenoid diversity in American species compared to Eurasian and African ones, suggesting adaptation to herbivory pressure.

MetaRiPPquest identifies RiPPs by combining genome/metagenome mining with mass spectrometry analysis, discovering 5 novel natural products from 6 datasets.