Proteomics Practical Course
Course 0940790810 in SS 2023
General Data
| Course Type | exercise |
|---|---|
| Semester Weekly Hours | 3 SWS |
| Organisational Unit | Chair of Bioanalytics (Prof. Küster) |
| Lecturers |
Bernhard Küster Christina Ludwig Annika Schneider Matthew The Stephanie Wilhelm |
| Dates |
Mon, 09:00–17:00, DG L 01 Tue, 09:00–17:00, DG L 01 Mon, 09:00–16:00, EG L 12 Mon, 09:00–16:00, DG L 01 |
Assignment to Modules
-
WZ2439: Proteomics: Analytische Grundlagen und Biomedizinische Anwendungen / Proteomics: Analytical Basics and Biomedical Applications
This module is included in the following catalogs:- Focus Area Bio-Sensors in M.Sc. Biomedical Engineering and Medical Physics
Further Information
Courses are together with exams the building blocks for modules. Please keep in mind that information on the contents, learning outcomes and, especially examination conditions are given on the module level only – see section "Assignment to Modules" above.
| additional remarks | In this exercise course, the participants will learn mass spectrometry-based methods and evaluation processes that allow identification as well as quantification of proteins. In every module the participants will work with data from a case study, which aims at identifying specific protein interaction partners of the clinical kinase inhibitor Lestaurtinib. Along with this case study, the participants will get familiar with the three steps required for every proteomic experiment: i) sample preparation, ii) mass spectrometric measurement, iii) (statistical) data analysis. The focus of the course will be on statistical data analysis. Here the participants will learn how to first manually annotate fragment ion spectra and then use “Sequence-Tags” for spectra interpretation. Further, they will learn how information about amino acid sequences and masses can be used for database searches. Finally, they will analyze data from an automated database search. Here the best matches between peptide sequences and acquired MS spectra were automatically identified and the underlying error rates were estimated. The used algorithms will be explained in detail. In the last part of the course the participants will learn that proteomics data can also be used for protein quantification. In this context, different visualization tools for quantitative data will be tested (principle component analysis, hierarchical clustering analysis, etc.) and a statistical evaluation of protein concentration differences between samples will be carried out (T-test analysis and volcano plots). All content stated above will be taught in a computer room and participants will perform exercises and data analyses themselves using software tools provided. There will be an intensive interaction between teachers and course participants. |
|---|---|
| Links | TUMonline entry |