The course will provide a general overview of LC-MS based untargeted metabolomics from study design to results and will be exemplified with its specific application in nutrition. It will be delivered using a mixture of lectures, hands-on data preparation and analysis, computer-based practical sessions, and discussions. Visits to wet labs and instructions on human sample preparation procedures is included but there is no practical lab work.
The students will go through common steps in a typical metabolomics study using a real-life case. This case study includes plasma (or urine) samples from a nutritional intervention. The sample preparation and analysis on UPLC-QTOF has been conducted and the students will further process and analyze the acquired data with various freeware tools (e.g. R, XCMS, MZmine etc). They will finally work on identification of relevant metabolites using manual analysis assisted by several web-based databases and structure elucidation tools. The course will conclude by presentations of reports generated by the students based on the case study.
The students should expect a fairly technical course with a strong focus on the hands-on data analysis abilities and data interpretation skills. Programming skills are not a prerequisite for entering the course and students are guided through the exercises. However, for students that are not familiar with R we expect them to explore the self-study curriculum based on short videos and texts that cover essential programming concepts. The project work has a high workload and hence evening work can be expected during the course week.
Content The course will provide a general overview of LC-MS based untargeted metabolomics from study design to results and will be exemplified with its specific application in nutrition. It will be delivered using a mixture of lectures, hands-on data preparation and analysis, computer-based practical sessions, and discussions. Visits to wet labs and instructions on human sample preparation procedures is included but with minimal hands-on.
The students will go through common steps in a typical metabolomics study using a real-life case. This case study includes collected plasma (or urine) samples from a nutritional intervention. The sample preparation and analysis on UPLC-QTOF has been conducted and the students will further process and analyze the acquired data with various freeware tools (e.g. R, XCMS, MZmine etc). They will finally work on identification of relevant metabolites using several web-based structure elucidation tools. The course will conclude by presentations of reports generated by the students based on the case study.
The course will be structured as initial short lectures on theory followed by hands-on exercises, which will teach the students to transfer the theoretical information to practice. The students should expect a fairly technical course with a strong focus on the hands-on data analysis abilities and data interpretation skills.
Content The course will provide an overview of LC-MS based untargeted metabolomics and its application in nutrition. It will be delivered using a mixture of lectures, hands-on data preparation and analysis, computer-based practical sessions, and discussions. Visits to wet labs and instructions on human sample preparation procedures is included but with minimal hands-on.
The students will go through common steps in a typical metabolomics study using a real-life case. This case study includes collected plasma (or urine) samples from a nutritional intervention. The sample preparation and analysis on UPLC-QTOF has been conducted and the students will further process and analyse the acquired data with various freeware tools (e.g. R, XCMS, MZmine and Metaboanalyst). They will finally work on identification of relevant metabolites using several web-based structure elucidation tools. The course will conclude by presentations of reports generated by the students based on the case study.
The course will be structured as initial short lectures on theory followed by hands-on exercises, which will teach the students to transfer the theoretical information to practice.
Content The course will provide an overview on LC-MS based untargeted metabolomics and its application in nutrition. It will be delivered using a mixture of lectures, hands-on data preparation and analysis, computer-based practical sessions, and discussions. Visits to wet labs and instructions on human sample preparation procedures is included but with minimal hands-on.
The students will go through common steps in a typical metabolomics study using a real-life case. This case study includes collected plasma (or urine) samples from a nutritional intervention. The sample preparation and analysis on UPLC-QTOF has been conducted and the students will further process and analyse the acquired data with various free-ware tools (MZmine, Workflow4Metabolomics and Metaboanalyst). They will finally work on identification of relevant metabolites using several web-based structure elucidation tools. The course will finalize by presentations of reports generated by the students based on the case study.
The course will be structured as initial short lectures on theory followed by hands-on exercises which will teach the students to transfer the theoretical information to practice.
Does anyone know if there is a way to direct access to the data in the NIST library? I would like to automate some things in R but the NIST library seems to be in some binary format. Have someone done something similar? Any hints?
The first MOOC of its kind, this course is an introduction to metabolomics principles and their applications in various fields of life sciences.
We will provide a summary of all steps in metabolomics research; from experimental design, sample preparation, analytical procedures, to data analysis. The course also provides case studies of various kinds of research samples to attract students that are not familiar with metabolomics, providing them enough explanation to utilize metabolomics technology for their respective research fields.
Several examples of metabolomics applications will be introduced throughout the lectures. These include examples within food science and technology, metabolic engineering, basic biology, introduction to imaging mass spectrometry, and application in medical science.
No previous knowledge on metabolomics is needed but we recommend that students have an undergraduate-level understanding of Biochemistry, Analytical Chemistry, and Biostatistics, and that they learn about basic principles of multivariable analysis prior to taking this course.
I was wondering if anyone knows a way to get good centroiding of Waters data that was recorded in profile mode? In MassLynx you can centroid a single spectrum and it looks like this as an example:
That seems reasonable by eye. I tried then using the centroiding in msconvert (Proteowizard). Here is the result:
What it has done is not finding the center of the peak but has chosen the top scan for the m/z. This is not very accurate and is in this case a difference of 15 ppm.
So my question is if someone knows a better way to centroid data that was already acquired? Msconvert can use vendor algorithms for centroiding for a lot of formats but apparently it is not available for waters data.
UPDATE: msconvert now supports vendor (AKA Waters) centroiding.
More information: We would like to invite you to Bio&Data, the first workshop of the newly established MOVISS – “Mountain Village Science Series” taking place in Vorau, Austria (Sep 20-23, 2017). MOVISS Bio&Data is different to the usual conferences. It is rather constructed as a small, problem-driven meeting, full of discussions and questions about how to deal with metabolomics data reasonably. In this way, we hope to constructively engage some of the greatest minds collaboratively in solving some of the challenges of the metabolomics and bioinformatics community.
Four sessions are planned, each devoted to a separate step of the metabolomics process; Design of Experiments, Analytical Analysis, Data Processing and Statistical Analysis in the biosciences will all be discussed including your data if you bring them for discussions.
We plan a summary of this discussion will be produced as a paper for publication to share within the wider metabolomics community. Finally, you can continue with the R Summer Schoolfrom September 25 – 27, 2017 in Vorau!
We are delighted to announce that Early bird registration is open for Metabolomics 2017 – the 13th Annual Conference of the Metabolomics Society. We look forward to seeing you in Brisbane! Please visit the new website for more information and take advantage of the early-bird pricing.
Website:Metabolomics 2017 Hosted by: The Metabolomics Society Where: Brisbane, Queensland, Australia When: June 26-29, 2017
Abstract submission information will be published within the next month. We look forward to seeing your latest findings!
The Society has obtained a group block of hotel rooms, visit the Hotel page of the website to view several different options. You are encouraged to book your hotel accommodation as soon as possible, since June is a very busy time inBrisbane.
Happy New Year! The Metabolomics 2017 Planning Team
Click here to learn more about this latest webinar by Dr. Stephan Hann.
Please register for “How well do I quantify? Concepts for method validation and evaluation of measurement uncertainty in metabolomics" to be held on February 01st, 2017 14:00 UTC/14:00 GMT at:
At time of writing there are 6 seats left. Don't miss out!
Content The course will provide an introduction to the LC-MS based nutritional metabolomics studies, aiming to cover key steps such as study design, sample collection and analysis, data handling methodologies and metabolite identification.
The students will go through common steps in a typical metabolomics study. Therefore, the major focus will be application of various free or commercial tools for data preprocessing, data analysis, and metabolite identification with computer based hands-on training using example datasets that will be provided to the students.
The course will finalize by presentations of reports generated by the students based on a case study.
SCIENCE, NordFOOD and NUGO homepages only. A limit of 16 participants has been set this year.
Learning outcome The aim of this course is to introduce the students to all phases in a nutritional metabolomics study and to train the student in the use of available tools for data handling. Both targeted and untargeted LC-MS methodologies will be thought, yet major focus of the course is untargeted LC-MS based metabolomics.
After completing the course the students should be able to:
• Evaluate the effect of study design on data handling and interpretation of final outcome • Suggest the sample type to analyze for a specific research question and propose the relevant sample collection and preparation procedure • Understand the basic principles of LC-MS technology • Carry out data preprocessing on using freely available tools such as MZmine • Perform univariate and multivariate analysis on R/MATLAB • Interpret the MS/MS spectra by utilizing available tools (e.g. CAMERA, MetFusion, MetFrag) and databases (e.g. HMDB, MassBank, MZCloud)
Teaching and learning methods Lectures, hands-on exercises, group discussions.
Lecturers • Lars O. Dragsted • Gözde Gürdeniz • Jan Stanstrup • Rastislav Monosik • Mads Vendelbo Lind
