Bioinformatics for Proteomics

NUMMER: 201911
MODULBEAUFTRAGTE:R: PD Dr. rer. medic. Martin Eisenacher
DOZENT:IN: PD Dr. Martin Eisenacher
FAKULTÄT: Medizinisches Proteom Center
SPRACHE: Deutsch
SWS: 3
ANGEBOTEN IM: jedes Wintersemester ab 2022/23


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


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.


• 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)


Passed examination


Recommended prior knowledge: English and basic programming skills