Universität Wien FIND

270192 PR Computational tools for (untargeted) metabolomics (2021W)

1.00 ECTS (1.00 SWS), SPL 27 - Chemie
Continuous assessment of course work


Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).


max. 15 participants
Language: German



6th and 7th December 2021, online


Aims, contents and method of the course

Untargeted metabolomics has gained much popularity in the recent years and the amount of data produced with modern LC-HRMS instruments increases exponentially thereby becoming a bigdata discipline just as its related disciplines genomics, transcriptomics and proteomics.
Currently, the biggest challenge is metabolite identification, which concerns targeted and untargeted approaches equally. In targeted applications there is a strong need for reliable metabolite identification using in-silico tools or large databases while in untargeted approaches the identification of novel compounds from analytical data (e.g. MS/MS and MSn spectra) is an active research field. Unfortunately, it looks like there is still a long way to go until these goals can be reached.
Similar to the development of new analytical instruments, the scientific community is also actively developing novel data processing tools and pipelines that provide the users with previously unmatched possibilities.

Aims and contents of the lecture
In this lecture the students will be introduced to novel data processing approaches for untargeted metabolomics research. The focus will be put on the computational tools and accompanying methods, their aims and how these can be utilized efficiently in experiments. The tools to be reviewed and presented to the students in this course include:
• Sum formula generation (tools for generating sum formulas for unidentified metabolites i.e. the dark matter)
• Identification levels (a methodology to quickly and comparably assess the confidence of metabolite identification/annotation)
• MASST (a tool similar to blast to search for MS/MS spectra in a steadily growing repository of metabolomics experiments)
• False-discovery-rate in metabolite annotation (an approach to control and judge the extend of in-silicio metabolite identification)
• Molecular networking (a tool to group/cluster putatively similar but unknown metabolites into clusters using similarities in their MS/MS spectra)
• MS2LDA (Substructure search with the aim to find and derive MS/MS fingerprints of substructures of putative metabolites)
• SIRIUS and CSI:FingerID compound search (a tool for large-scale in-silico identification of metabolites)
• In-silico MS/MS prediction tools

This course will be conducted via presentations, practical work either alone or in form of small groups, discussions between the students and student presentations, among others.

Assessment and permitted materials

Methods used to evaluate the students’ performances:
• Active participation during the course
• Written final exam on the (presented) contents
Should it be necessary, students may be invited to an interview with the course instructor. This interview will then also count for the final mark.

Minimum requirements and assessment criteria

Students must participate in at least 75% of the course.
In order to pass the course, students must reach at least 50% of the maximum possible points. Additionally, each evaluation criteria must be evaluated positively.

Students can earn a maximum of 100 points during the lecture. These are divided into:
• Active participation: 30 points
• Final exam: 70 points

The final marks are:
• 1 (A): 100 - 89 points
• 2 (B): 88 - 76 points
• 3 (C): 75 - 63 points
• 4 (D): 62 - 50 points
• 5 (F): 49 - 0 points
(Points will be rounded in favor of the student.)

Examination topics

The contents of the lectures and practical work.

Reading list

Association in the course directory

AN-2, BC-1, CHE II-1

Last modified: We 29.09.2021 13:49