Jekyll 2025-05-19T08:39:34+00:00 https://training.galaxyproject.org/training-material/topics/metabolomics/feed.xml Recently added tutorials, slides, FAQs, and events in the metabolomics topic https://training.galaxyproject.org/training-material/assets/images/GTN-60px.png https://training.galaxyproject.org/training-material/topics/metabolomics/tutorials/mfassignr/workflows/main_workflow.html 2025-05-19T08:39:34+00:00

This workflow can be used to assign multi-element molecular formulas to ultrahigh resolution mass spectra.

RECETOX, MUNI Kristina Gomoryova https://training.galaxyproject.org/training-material/hall-of-fame/KristinaGomoryova/ Helge Hecht https://training.galaxyproject.org/training-material/hall-of-fame/hechth/ https://training.galaxyproject.org/training-material/topics/metabolomics/tutorials/mfassignr/tutorial.html 2025-05-19T08:39:34+00:00

This training covers the multi-element molecular formula (MF) assignment using the MFAssignR tool. It was originally developed by Schum et al. 2020 for the analysis of untargeted mass spectrometry data coming from complex environmental mixtures. The package contains several functions including noise assessment, isotope filtering, internal mass recalibration, and formula assignment.

Kristina Gomoryova https://training.galaxyproject.org/training-material/hall-of-fame/KristinaGomoryova/ Helge Hecht https://training.galaxyproject.org/training-material/hall-of-fame/hechth/ Simeon Schum https://training.galaxyproject.org/training-material/hall-of-fame/skschum/ Helge Hecht https://training.galaxyproject.org/training-material/hall-of-fame/hechth/ Kristina Gomoryova https://training.galaxyproject.org/training-material/hall-of-fame/KristinaGomoryova/ Saskia Hiltemann https://training.galaxyproject.org/training-material/hall-of-fame/shiltemann/ Pavankumar Videm https://training.galaxyproject.org/training-material/hall-of-fame/pavanvidem/ https://training.galaxyproject.org/training-material/topics/proteomics/tutorials/multiGSEA-tutorial/tutorial.html 2025-03-10T15:22:38+00:00

The multiGSEA package was designed to run a robust GSEA-based pathway enrichment for multiple omics layers (Canzler and Hackermüller, 2020) Canzler and Hackermüller 2020. The enrichment is calculated for each omics layer separately and aggregated p-values are calculated afterwards to derive a composite multi-omics pathway enrichment.

Thorben Stehling https://training.galaxyproject.org/training-material/hall-of-fame/tStehling/ Matthias Bernt https://training.galaxyproject.org/training-material/hall-of-fame/bernt-matthias/ Björn Grüning https://training.galaxyproject.org/training-material/hall-of-fame/bgruening/ Saskia Hiltemann https://training.galaxyproject.org/training-material/hall-of-fame/shiltemann/ https://training.galaxyproject.org/training-material/topics/metabolomics/tutorials/qcxms-predictions/workflows/End-to-end-EI+-mass-spectra-prediction-workflow-using-QCxMS.html 2024-10-01T20:12:43+00:00

This workflow predict in-silico mass spectra using semi-empirical quantum physics method.

RECETOX SpecDat https://training.galaxyproject.org/training-material/topics/metabolomics/tutorials/qcxms-predictions/slides.html 2024-10-01T20:12:43+00:00

name:qcxms-spectra-predictions

Helge Hecht https://training.galaxyproject.org/training-material/hall-of-fame/hechth/ Wudmir Rojas https://training.galaxyproject.org/training-material/hall-of-fame/wverastegui/ Zargham Ahmad https://training.galaxyproject.org/training-material/hall-of-fame/zargham-ahmad/ Julia Jakiela https://training.galaxyproject.org/training-material/hall-of-fame/wee-snufkin/ Saskia Hiltemann https://training.galaxyproject.org/training-material/hall-of-fame/shiltemann/ Melanie Föll https://training.galaxyproject.org/training-material/hall-of-fame/foellmelanie/ Julia Jakiela https://training.galaxyproject.org/training-material/hall-of-fame/wee-snufkin/ https://training.galaxyproject.org/training-material/topics/metabolomics/tutorials/qcxms-predictions/tutorial.html 2024-10-01T20:12:43+00:00

Mass spectrometry (MS) is a powerful analytical technique used in many fields, including proteomics, metabolomics, drug discovery and many more areas relying on compound identifications. Even though nowadays MS is a standard and popular method, there are many compounds which lack experimental spectra. In those cases, predicting mass spectra from the chemical structure can reveal useful information, help in compound identification and expand the spectral databases, improving the accuracy and efficiency of database search. [Zhu and Jonas 2023, Allen et al. 2016].

Julia Jakiela https://training.galaxyproject.org/training-material/hall-of-fame/wee-snufkin/ Helge Hecht https://training.galaxyproject.org/training-material/hall-of-fame/hechth/ Saskia Hiltemann https://training.galaxyproject.org/training-material/hall-of-fame/shiltemann/ Melanie Föll https://training.galaxyproject.org/training-material/hall-of-fame/foellmelanie/ Julia Jakiela https://training.galaxyproject.org/training-material/hall-of-fame/wee-snufkin/ Pavankumar Videm https://training.galaxyproject.org/training-material/hall-of-fame/pavanvidem/ https://training.galaxyproject.org/training-material/topics/metabolomics/tutorials/gc_ms_with_xcms/workflows/main_workflow.html 2023-05-08T12:44:32+00:00

XCMS and RAMClustR based workflow for data processing and annotation using library matching via matchms.

RECETOX https://training.galaxyproject.org/training-material/hall-of-fame/RECETOX/ https://training.galaxyproject.org/training-material/topics/metabolomics/tutorials/gc_ms_with_xcms/tutorial.html 2023-05-08T12:44:32+00:00

The study of metabolites in biological samples is routinely defined as metabolomics. Metabolomics’ studies based on untargeted mass spectrometry provide the capability to investigate metabolism on a global and relatively unbiased scale in comparison to traditional targeted studies focused on specific pathways of metabolism and a small number of metabolites. The untargeted approach enables the detection of thousands of metabolites in hypothesis-generating studies and links previously unknown metabolites with biologically important roles Patti et al. 2012. There are two major issues in contemporary mass spectrometry-based metabolomics: the first is enormous loads of signal generated during the experiments, and the second is the fact that some metabolites in the studied samples may not be known to us. These obstacles make the task of processing and interpreting the metabolomics data a cumbersome and time-consuming process Nash and Dunn 2019.

Matej Troják https://training.galaxyproject.org/training-material/hall-of-fame/xtrojak/ Helge Hecht https://training.galaxyproject.org/training-material/hall-of-fame/hechth/ Maxim Skoryk https://training.galaxyproject.org/training-material/hall-of-fame/maximskorik/ Helena Rasche https://training.galaxyproject.org/training-material/hall-of-fame/hexylena/ Matej Troják https://training.galaxyproject.org/training-material/hall-of-fame/xtrojak/ Mélanie Petera https://training.galaxyproject.org/training-material/hall-of-fame/melpetera/ Saskia Hiltemann https://training.galaxyproject.org/training-material/hall-of-fame/shiltemann/ Björn Grüning https://training.galaxyproject.org/training-material/hall-of-fame/bgruening/ Pavankumar Videm https://training.galaxyproject.org/training-material/hall-of-fame/pavanvidem/ https://training.galaxyproject.org/training-material/topics/metabolomics/tutorials/lcms-dataprocessing/tutorial.html 2021-12-14T20:39:51+00:00

Metabolomics is a -omic science known for being one of the most closely related to phenotypes.

Mélanie Petera https://training.galaxyproject.org/training-material/hall-of-fame/melpetera/ Workflow4Metabolomics core team https://training.galaxyproject.org/training-material/hall-of-fame/workflow4metabolomics/ Helena Rasche https://training.galaxyproject.org/training-material/hall-of-fame/hexylena/ Saskia Hiltemann https://training.galaxyproject.org/training-material/hall-of-fame/shiltemann/ Melanie Föll https://training.galaxyproject.org/training-material/hall-of-fame/foellmelanie/ Pavankumar Videm https://training.galaxyproject.org/training-material/hall-of-fame/pavanvidem/ https://training.galaxyproject.org/training-material/topics/metabolomics/tutorials/gcms/tutorial.html 2021-09-22T12:09:15+00:00

You may already know that there are different types of -omic sciences; out of these, metabolomics is most closely related to phenotypes. Metabolomics involves the study of different types of matrices, such as blood, urine, tissues, in various organisms including plants. It focuses on studying the very small molecules which are called metabolites, to better understand matters linked to the metabolism. However, studying metabolites is not a piece of cake since it requires several critical steps which still have some major bottlenecks. Metabolomics is still quite a young science, and has many kinds of specific challenges.

Julien Saint-Vanne https://training.galaxyproject.org/training-material/hall-of-fame/jsaintvanne/ Yann Guitton https://training.galaxyproject.org/training-material/hall-of-fame/yguitton/ Yann Guitton https://training.galaxyproject.org/training-material/hall-of-fame/yguitton/ Mélanie Petera https://training.galaxyproject.org/training-material/hall-of-fame/melpetera/ Julien Saint-Vanne https://training.galaxyproject.org/training-material/hall-of-fame/jsaintvanne/ Workflow4Metabolomics core team https://training.galaxyproject.org/training-material/hall-of-fame/workflow4metabolomics/ Saskia Hiltemann https://training.galaxyproject.org/training-material/hall-of-fame/shiltemann/ Cristóbal Gallardo https://training.galaxyproject.org/training-material/hall-of-fame/gallardoalba/ Pavankumar Videm https://training.galaxyproject.org/training-material/hall-of-fame/pavanvidem/ https://training.galaxyproject.org/training-material/topics/metabolomics/tutorials/msi-analyte-distribution/workflows/wf_MSI_distribution.html 2020-11-01T18:32:51+00:00

Mass spectrometry imaging: Examining the spatial distribution of analytes

https://training.galaxyproject.org/training-material/topics/metabolomics/tutorials/lcms-preprocessing/slides.html 2020-02-04T08:04:53+00:00

name:raw-to-matrix

Jean-François Martin https://training.galaxyproject.org/training-material/hall-of-fame/jfrancoismartin/ Mélanie Petera https://training.galaxyproject.org/training-material/hall-of-fame/melpetera/ Saskia Hiltemann https://training.galaxyproject.org/training-material/hall-of-fame/shiltemann/ Gildas Le Corguillé https://training.galaxyproject.org/training-material/hall-of-fame/lecorguille/ Melanie Föll https://training.galaxyproject.org/training-material/hall-of-fame/foellmelanie/ Mélanie Petera https://training.galaxyproject.org/training-material/hall-of-fame/melpetera/ Björn Grüning https://training.galaxyproject.org/training-material/hall-of-fame/bgruening/ Cristóbal Gallardo https://training.galaxyproject.org/training-material/hall-of-fame/gallardoalba/ https://training.galaxyproject.org/training-material/topics/metabolomics/tutorials/lcms-preprocessing/tutorial.html 2020-02-04T08:04:53+00:00

Metabolomics is a -omic science known for being one of the most closely related to phenotypes.

Mélanie Petera https://training.galaxyproject.org/training-material/hall-of-fame/melpetera/ Jean-François Martin https://training.galaxyproject.org/training-material/hall-of-fame/jfrancoismartin/ Gildas Le Corguillé https://training.galaxyproject.org/training-material/hall-of-fame/lecorguille/ Workflow4Metabolomics core team https://training.galaxyproject.org/training-material/hall-of-fame/workflow4metabolomics/ Helena Rasche https://training.galaxyproject.org/training-material/hall-of-fame/hexylena/ Saskia Hiltemann https://training.galaxyproject.org/training-material/hall-of-fame/shiltemann/ Mélanie Petera https://training.galaxyproject.org/training-material/hall-of-fame/melpetera/ Gildas Le Corguillé https://training.galaxyproject.org/training-material/hall-of-fame/lecorguille/ Melanie Föll https://training.galaxyproject.org/training-material/hall-of-fame/foellmelanie/ Björn Grüning https://training.galaxyproject.org/training-material/hall-of-fame/bgruening/ Pavankumar Videm https://training.galaxyproject.org/training-material/hall-of-fame/pavanvidem/ https://training.galaxyproject.org/training-material/topics/metabolomics/tutorials/lcms/workflows/main_workflow.html 2019-07-02T07:12:08+00:00

Mass spectrometry: LC-MS analysis

https://training.galaxyproject.org/training-material/topics/metabolomics/tutorials/lcms/tutorial.html 2019-07-02T07:12:08+00:00

You may already know that there are different types of -omic sciences; out of these, metabolomics is most closely related to phenotypes.

Mélanie Petera https://training.galaxyproject.org/training-material/hall-of-fame/melpetera/ Gildas Le Corguillé https://training.galaxyproject.org/training-material/hall-of-fame/lecorguille/ Jean-François Martin https://training.galaxyproject.org/training-material/hall-of-fame/jfrancoismartin/ Yann Guitton https://training.galaxyproject.org/training-material/hall-of-fame/yguitton/ Workflow4Metabolomics core team https://training.galaxyproject.org/training-material/hall-of-fame/workflow4metabolomics/ Helena Rasche https://training.galaxyproject.org/training-material/hall-of-fame/hexylena/ Saskia Hiltemann https://training.galaxyproject.org/training-material/hall-of-fame/shiltemann/ Gildas Le Corguillé https://training.galaxyproject.org/training-material/hall-of-fame/lecorguille/ Björn Grüning https://training.galaxyproject.org/training-material/hall-of-fame/bgruening/ Pavankumar Videm https://training.galaxyproject.org/training-material/hall-of-fame/pavanvidem/ https://training.galaxyproject.org/training-material/topics/metabolomics/tutorials/msi-finding-nglycans/workflows/msi-finding-diff-analytes.html 2019-04-13T15:52:01+00:00

Mass spectrometry imaging: Finding differential analytes

https://training.galaxyproject.org/training-material/topics/metabolomics/tutorials/msi-finding-nglycans/tutorial.html 2019-04-13T15:52:01+00:00

Mass spectrometry imaging (MSI) is applied to measure the spatial distribution of hundreds of biomolecules in a sample. A mass spectrometer scans over the entire sample and collects a mass spectrum every 5-200 µm. This results in thousands of spots (or pixels) for each of which a mass spectrum is acquired. Each mass spectrum consists of hundreds of analytes that are measured by their mass-to-charge (m/z) ratio. For each analyte the peak intensity in the mass spectra of every pixel is known and can be set together to map the spatial distribution of the analyte in the sample.

Melanie Föll https://training.galaxyproject.org/training-material/hall-of-fame/foellmelanie/ Björn Grüning https://training.galaxyproject.org/training-material/hall-of-fame/bgruening/ Simon Bray https://training.galaxyproject.org/training-material/hall-of-fame/simonbray/ Melanie Föll https://training.galaxyproject.org/training-material/hall-of-fame/foellmelanie/ Bérénice Batut https://training.galaxyproject.org/training-material/hall-of-fame/bebatut/ Saskia Hiltemann https://training.galaxyproject.org/training-material/hall-of-fame/shiltemann/ Pavankumar Videm https://training.galaxyproject.org/training-material/hall-of-fame/pavanvidem/ https://training.galaxyproject.org/training-material/topics/metabolomics/tutorials/msi-analyte-distribution/tutorial.html 2019-02-21T17:36:18+00:00

Mass spectrometry imaging (MSI) is applied to measure the spatial distribution of hundreds of biomolecules in a sample. A mass spectrometer scans over the entire sample and collects a mass spectrum every 5-200 µm. This results in thousands of spots (or pixels) for each of which a mass spectrum is acquired. Each mass spectrum consists of hundreds of analytes that are measured by their mass-to-charge (m/z) ratio. For each analyte the peak intensity in the mass spectra of every pixel is known and can be set together to map the spatial distribution of the analyte in the sample.

Melanie Föll https://training.galaxyproject.org/training-material/hall-of-fame/foellmelanie/ Maren Stillger https://training.galaxyproject.org/training-material/hall-of-fame/MarenStillger/ Helena Rasche https://training.galaxyproject.org/training-material/hall-of-fame/hexylena/ Saskia Hiltemann https://training.galaxyproject.org/training-material/hall-of-fame/shiltemann/ Björn Grüning https://training.galaxyproject.org/training-material/hall-of-fame/bgruening/ Bérénice Batut https://training.galaxyproject.org/training-material/hall-of-fame/bebatut/ Simon Bray https://training.galaxyproject.org/training-material/hall-of-fame/simonbray/ Melanie Föll https://training.galaxyproject.org/training-material/hall-of-fame/foellmelanie/ Pavankumar Videm https://training.galaxyproject.org/training-material/hall-of-fame/pavanvidem/ https://training.galaxyproject.org/training-material/topics/metabolomics/tutorials/introduction/slides.html 2018-11-20T17:16:31+00:00

Metabolomics

Gildas Le Corguillé https://training.galaxyproject.org/training-material/hall-of-fame/lecorguille/ Cécile Canlet https://training.galaxyproject.org/training-material/hall-of-fame/cecilecanlet/ Bérénice Batut https://training.galaxyproject.org/training-material/hall-of-fame/bebatut/ Simon Bray https://training.galaxyproject.org/training-material/hall-of-fame/simonbray/ Melanie Föll https://training.galaxyproject.org/training-material/hall-of-fame/foellmelanie/ Cristóbal Gallardo https://training.galaxyproject.org/training-material/hall-of-fame/gallardoalba/ Saskia Hiltemann https://training.galaxyproject.org/training-material/hall-of-fame/shiltemann/ RJMW https://training.galaxyproject.org/training-material/hall-of-fame/RJMW/ Gildas Le Corguillé https://training.galaxyproject.org/training-material/hall-of-fame/lecorguille/ Helena Rasche https://training.galaxyproject.org/training-material/hall-of-fame/hexylena/