GNPS - molecular networking of Trillium tschonoskii.
Ontology highlight
ABSTRACT: This is a dataset used for the orchestration of molecular networking which led the discovery of polyacetylated 18-norspirostanol saponins from Trillium tschonoskii.
Project description:This is a dataset used for the orchestration of molecular networking which led the discovery of polyacetylated 18-norspirostanol saponins from Trillium tschonoskii.
Project description:Molecular networking has become a key method to visualize and annotate the chemical space in non-targeted mass spectrometry data. We present feature-based molecular networking (FBMN) as an analysis method in the Global Natural Products Social Molecular Networking (GNPS) infrastructure that builds on chromatographic feature detection and alignment tools. FBMN enables quantitative analysis and resolution of isomers, including from ion mobility spectrometry.
Project description:Molecular networking connects mass spectra of molecules based on the similarity of their fragmentation patterns. However, during ionization, molecules commonly form multiple ion species with different fragmentation behavior. As a result, the fragmentation spectra of these ion species often remain unconnected in tandem mass spectrometry-based molecular networks, leading to redundant and disconnected sub-networks of the same compound classes. To overcome this bottleneck, we develop Ion Identity Molecular Networking (IIMN) that integrates chromatographic peak shape correlation analysis into molecular networks to connect and collapse different ion species of the same molecule. The new feature relationships improve network connectivity for structurally related molecules, can be used to reveal unknown ion-ligand complexes, enhance annotation within molecular networks, and facilitate the expansion of spectral reference libraries. IIMN is integrated into various open source feature finding tools and the GNPS environment. Moreover, IIMN-based spectral libraries with a broad coverage of ion species are publicly available.
Project description:This dataset contains the raw data used for MZmine processing and Feature-based molecular networking of semi-purified fractions obtained from A. timonensis used for bioactive molecular networking to reveal contribution of sulfonolipids to observed biological activity.
Project description:Fungal endophytes often live in symbiotic relationships with various plant hosts, conferring positive effects to their host organism. These endophytes frequently produce a wide variety of secondary metabolites with bioactivities that are often responsible for the beneficial effects seen in the host, such as antifungal or anti-insectan activity. A large group of fungal endophytes isolated from Canadian fruit crops including blueberries, raspberries, cranberries, grapes, and pears, was analyzed using molecular networking by GNPS in an effort to simplify the process of examining a large dataset. Molecular networking increased the speed and efficiency of examining this dataset, permitting the dereplication of 60 known compounds and the discovery of seven putative novel compounds, which will be purified, characterized, and tested for bioactivity in future studies.
Project description:GNPS Feature-Based Molecular Networking Workshop - American Gut subset with metadata for plant consumption
See manuscript here: https://msystems.asm.org/content/3/3/e00031-18
Project description:Example dataset for Methods in Molecular Biology Chapter - Feature Based Molecular Networking for Metabolite Annotation. This dataset includes the LC-MS/MS raw data (Bruker .d file format and centroided mzXML file format), metadata table used for the ste-by-step instructions, a batch file for MZmine2 data processing, and all resulting files from the MZmine2 and GNPS processing.
Project description:Mass spectrometry is the current technique of choice in studying drug metabolism. High-resolution mass spectrometry (HR-MS) in combination with fragment analysis (MS/MS) has the potential to contribute to rapid advances in this field. However, the data emerging from such fragmentation spectra pose challenges to downstream analysis, given their complexity and size. Here we apply a molecular networking approach to seek drugs and their metabolites, in fragmentation spectra from urine derived from a cohort of 26 patients on antihypertensive therapy. In total, 165 separate drug metabolites were found and structurally annotated (17 by spectral matching and 122 by classification based on a clustered fragmentation pattern). The clusters could be traced to 13 drugs including the known antihypertensives verapamil, losartan and amlopidine. The molecular networking approach also generated networks of endogenous metabolites, including carnitine derivatives, and conjugates containing glutamine, glutamate and trigonelline. The approach offers unprecedented capability in the untargeted identification of drugs and their metabolites at the population level and has great potential to contribute to understanding stratified responses to drugs where differences in drug metabolism may determine treatment outcome. Keywords: Antihypertensive drugs, Drug metabolism, Fragmentation, High-resolution mass spectrometry, Metabolomics, Urine.
Project description:Molecular networking and pattern-based genome mining improves discovery of biosynthetic gene clusters and their products from Salinispora