Difference between revisions of "EMN Webinars"

From Metabolomics Society Wiki
Jump to: navigation, search
Line 126: Line 126:
==[http://metabolomicssociety.org/resources/videos/88-videos/258-2017-emn-webinars-public| How well do I quantify? Machine learning powered metabolomic network analysis]==
==[http://metabolomicssociety.org/resources/videos/88-videos/258-2017-emn-webinars-public| Machine learning powered metabolomic network analysis]==
By: Dr. Dmitry Grapov  
By: Dr. Dmitry Grapov  

Revision as of 01:14, 22 April 2018

The Early-Career Members Network (EMN) Committee has organized webinars since 2015 on a variety of metabolomics-related topics. This page provides a summary of all webinars to date with links to the webinar where available.


Metabolomics: only suitable for multidisciplinary teams

By: Dr. Oscar Yanes

Date: 29 Jan 2015

Metabolomics is defined as the comprehensive and quantitative analysis of metabolites in living organisms. Among the omic sciences, metabolomics is possibly the most multidisciplinary of all, involving knowledge about electronic engineering and signal processing, analytical and organic chemistry, biostatistics and statistical physics, and biochemistry and cell metabolism. Here an untargeted metabolomics workflow will be detailed that provides examples of this multidisciplinarity.

Large-scale, computational and empirical UHPLC-MS-SPE-NMR annotation of plant metabolomes

By: Dr. Lloyd Sumner

Date: 5 March 2015

Integrated metabolomics is a revolutionary systems biology tool for understanding plant metabolism and elucidating gene function. Although the vast utility of metabolomics is well documented in the literature, its full scientific promise has not yet been realized due to multiple technical challenges. The number one, grand challenge of metabolomics is the large-scale confident chemical identification of metabolites. To address this challenge, we have developed sophisticated computational and empirical metabolomics tools for the systematic and biological directed annotation of plant metabolomes. This presentation will introduce novel software entitled Plant Metabolite Annotation Toolbox (PlantMAT) and a sophisticated UHPLC-MS-SPENMR instrumental ensemble that are being used for ‘sequencing’ the first plant metabolomes of the model plant systems Arabidopsis and Medicago truncatula.

Metabolomics and systems biology: models as guide and natural integrator

By: Prof. Bas Teusink

Date: 14 April 2015

In this webinar, I will discuss the use of mathematical models in guiding targeted and (semi-) untargeted metabolomics efforts. I will show how genome-scale metabolic models can be used as a data integration platform - also for metabolomics data. I will provide an example from medium optimisation in a biotechnological context. My message is that the use of models upfront combined with quantitative and well-timed metabolomics is often much more effective than simply generating lots of data and subsequent statistical analysis.

Metabolomics with high resolution mass spectrometry: from spectral databases to biomarker discovery

By: Dr. Christophe Junot

Date: 12 June 2015

Since the middle of the 2000’s, high resolution mass spectrometry (HRMS) is widely used in metabolomics, mainly because the detection and identification of metabolites are improved compared to low resolution instruments. Thanks to their versatility, HRMS instruments are the most appropriate to achieve an optimal metabolome coverage, at the border of other omics fields such as lipidomics and glycomics. The aim of this talk will be to present HRMS based tools for metabolomics and lipidomics developed at the laboratory, and their relevance to the field of biomarker discovery for the diagnosis and follow-up of pathologies.

Metabolomics and Beyond: Challenges and Strategies for Next-gen Omic Analyses

By: Dr. Dmitry Grapov

Date: September 15, 2015

Robust interpretation of experimental results measuring discreet biological domains remains a significant challenge in the face of complex biochemical regulation processes such as organismal versus tissue versus cellular metabolism, epigenetics, and protein post-translational modification. Integration of analyses carried out across multiple measurement or omic platforms is an emerging approach to help address these challenges. The following presentation will focus on key challenges faced by metabolomics researchers in the areas large-scale studies data normalization, multivariate analysis, visualization and omics data integration.


How close are we to metabolomics reproducibility? Data sharing, data standards and workflows for metabolomics

By: Dr. Reza Salek

Date: March 24, 2016

Chromatography and Metabolomics: What’s the Point?

By: Dr. Karl Burgess

Date: April 29, 2016

The key to both chromatography and mass spectrometry is the separation of chemical species based on their physicochemical properties. As metabolomics researchers, we look to improved chromatography so enable us to detect compounds we previously had trouble with, to reduce the enormous complexity of the samples we analyse, and to clean up samples before or during analysis. Advances in mass spectrometry bring us greater sensitivity, better selectivity and a toolbox of techniques to aid in identification of biochemicals. With all these advantages come many disadvantages - poor reproducibility, compound bias and contamination. In this webinar, I'll explore chromatography and mass spectrometry with a critical eye. How can we improve them? Do we need them in every experiment? And really, what's the point of metabolomics at all?

From Features to Compounds: How To Get Started and Move Forward

By: Dr. Jan Stanstrup

Date: May 27, 2016

In the untargeted analysis of complex mixtures the identification of compounds is the fundamental step enabling the results to be put in biological context. It is therefore of crucial importance for early career scientists approaching the field of metabolomics to familiarize themselves with this step. Many tools have been developed to aid identification; however, compound identification still constitutes one of the main bottlenecks in metabolomics and still requires substantial amounts of manual work. This webinar will go through the basic concepts used in MS-based compound identification and will introduce a number of relevant tools and databases allowing researcher to approach identification in a systematic way. The webinar is in particular dedicated to those researchers coming into the field of metabolomics that are not familiar with the methods used for compound identification and for which getting started can be a daunting and time-consuming challenge.

Targeted High-throughput Lipidomics Platform: Development and Application

By: Dr. Peter Meikle

Date: July 27, 2016

Lipidomics, full analysis of lipid species and their biological roles with respect to health and diseases, has attracted increasing attention of biological and analytical scientific community. Knowing and understanding steps involved in lipidomics experimental workflow is essential for successful outcome. The Metabolomics Laboratory at Baker IDI Heart and Diabetes Institute (Melbourne, Australia) has a focus on the dyslipidemia and altered lipid metabolism associated with obesity, diabetes and cardiovascular disease and its relationship to the pathogenesis of these disease states. The laboratory has developed a targeted lipidomics platform that is able to quantify over 500 lipid species in 15 minutes using liquid chromatography coupled to electrospray ionisation tandem mass spectrometry. This platform is being applied to identify new approaches to early diagnosis and risk assessment as well as the development of new lipid modulating therapies for chronic disease. With illustration from this well-established lipidomics platform, this webinar will discuss the development of targeted high-throughput lipidomics platform, including selection and characterisation of lipid species, development of chromatography and quality control in the analysis of large sample sets. The presentation will draw on specific examples to highlight the application to large cohort studies and the Institute’s work to develop new therapeutic strategies for cardiovascular disease.

Mass Spectral Libraries

By: Dr. Emma Schymanski

Date: October 6, 2016

Mass spectrometry is applied in diverse ways in metabolomics research and the mass spectrum of a small molecule can act as a fingerprint for identification. Just as the scientific questions in metabolomics vary, there is a diverse set of mass spectral libraries available to assist in the identification of metabolites and other small molecules. This webinar aims to provide listeners with a brief overview of several different mass spectral resources, including a personal view on pros and cons of the different options – providing a basis for listeners to choose the resource(s) that may best suit their investigation and needs. Additional information about substance overlap, spectral matching, identification confidence and spectral exchange will also be given – as well as some factors to consider carefully. Finally, some perspectives towards in silico identification without spectral libraries will be given to lead into a topic for a future webinar.

Smarter ways to clean LC-MS

By: Assoc. Prof. Carl Brunius

Date: November 17, 2016

LC-MS is the most frequently used technique for untargeted metabolomics owing to broad coverage of metabolites, high sensitivity and simple sample preparation. However, instrument data tends to have high noise contribution from drift in signal intensity, mass accuracy and retention times. This noise has both within batch and between batch contributions and results in reduced measurement repeatability and reproducibility. The power to detect biological responses may thus be decreased and interpretations consequently obscured. Dr. Carl Brunius (the speaker) is involved in developing procedures to address and correct for within- and between-batch variability in processing multiple-batch untargeted LC-MS metabolomics data to increase their quality. This webinar session will talk about (i) alignment and merging of LC-MS features that are systematically misaligned between batches and (ii) within batch intensity drift correction that allows multiple drift patterns within batch. These algorithms will be applied on authentic data, resulting in improved peak picking performance and decreased noise in the dataset. All algorithms are developed as open source and are, together with example data, freely available as an R package from https://GitLab.com/CarlBrunius/batchCorr.


How well do I quantify? Concepts for method validation and evaluation of measurement uncertainty in metabolomics

By: Assoc. Prof. Stephan Hann

Date: March 24, 2017

This webinar will give an introduction to the basic terminology and principles of validation and measurement uncertainty in metabolomics. It will be demonstrated how validation parameters are determined in selected examples (e.g. LC-MS/MS, GC-MS/MS) for quantitative metabolomic analysis. Different quantification approaches will be overviewed in detail, and tips on choosing the most appropriate analytical strategies to answer metabolome-level hypotheses will be given. Finally, a workflow for estimating measurement uncertainty in metabolomics will be presented and discussed.

Machine learning powered metabolomic network analysis

By: Dr. Dmitry Grapov

Date: May 30, 2017

Metabolomic network analysis can be used to interpret experimental results within a variety of contexts including: biochemical relationships, structural and spectral similarity and empirical correlation. Machine learning is useful for modeling relationships in the context of pattern recognition, clustering, classification and regression based predictive modeling. The combination of developed metabolomic networks and machine learning based predictive models offer a unique method to visualize empirical relationships while testing key experimental hypotheses. The following presentation focuses on data analysis, visualization, machine learning and network mapping approaches used to create richly mapped metabolomic networks.