Louis-Félix Nothias-Scaglia

The paper develops a deep learning model to predict MS2 identifiability using MS1 data and acquisition parameters, achieving 90% recovery of known identifiable spectra.

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

The paper develops a functional metabolomics strategy to annotate metabolites in the human scalp and identifies the metabolic niche of Staphylococcus epidermidis.

Inventa identifies chemically novel natural products in plant extracts by integrating mass spectrometry data and literature reports, demonstrating its utility with a Celastraceae family example.

MEMO enables clustering of chemodiverse samples using LC-MS/MS profiles without feature alignment, achieving performance comparable to state-of-the-art methods faster.

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

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.

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.

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.

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

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.

The paper develops methods to estimate false discovery rates in untargeted metabolomics annotations and shows how adjusting spectral matching parameters can significantly increase the number of annotations.