| NUMMER: | 201911 |
| KÜRZEL: | BioInfPro |
| MODULBEAUFTRAGTE:R: | PD Dr. rer. medic. Martin Eisenacher |
| DOZENT:IN: | PD Dr. Martin Eisenacher |
| FAKULTÄT: | Medizinisches Proteom Center |
| SPRACHE: | Deutsch |
| SWS: | 3 |
| CREDITS: | 5 |
| ANGEBOTEN IM: | jedes Wintersemester ab 2022/23 |
LERNFORM
Lecture: slide-based lecture.
Tutorial: Solution of practical exercises using real example data as homework, programming tasks, group work, live-presentation of code and software
LERNZIELE
After the successful completion of this module• the students have become familiar with knowledge of protein biochemistry,
• they are able to explain the principles of mass spectrometry as the key technology of proteomics,
• they are able to explain the current methods of bioinformatics for proteomics that are used for the analysis of raw data (i.e., mass spectra),
• they are able to identify and quantify peptides or proteins, respectively, and to interpret them biologically,
• they understand the underlying algorithmic and statistical concepts of these methods,
• they are able to use proteomics-specific software, tools, and / or algorithms,
• they are able to design and program own solutions,
• and they are able to apply the discussed software tools and methods to real data and problems.
INHALT
• Basics of protein biochemistry• Properties of amino acids
• Basics of mass spectrometry
• Protein databases
• Tryptic and in silico digest of proteins
• Principles of spectra identification search engines and scores
• Quality control
• Estimation of the false discovery rate (FDR) using the target-decoy-approach
• Protein inference
• Single / multiple / parallel reaction monitoring (SRM / MRM / PRM)
• Data independent acquisition (DIA)
• Protein quantification (label-based and label-free techniques)
• Preprocessing of quantitative data
• Basic statistics / statistics for the comparison of experimental groups
• Artificial Intelligence / Machine learning / Classification (e. g. for biomarker discovery)
• Protein overrepresentation / enrichment analysis
• Software tools used in bioinformatics for proteomics (Tutorial)
• Practical (programming) tasks (Tutorial)
VORAUSSETZUNGEN CREDITS
Passed examination