| NUMMER: | 201911 |
| KÜRZEL: | BioInfPro1 |
| MODULBEAUFTRAGTE:R: | PD Dr. rer. medic. Martin Eisenacher |
| DOZENT:IN: | PD Dr. Martin Eisenacher |
| FAKULTÄT: | Medizinisches Proteom Center |
| SPRACHE: | Deutsch |
| SWS: | 3 SWS |
| CREDITS: | 5 CP |
| ANGEBOTEN IM: | jedes Wintersemester |
PRÜFUNGEN
| FORM: | schriftlich oder mündlich |
| TERMIN: | Siehe Prüfungsamt. |
LERNFORM
Lecture: slide-based lecture. Tutorial: Solution of small practical exercises using real example
data as homework, programming tasks, group work, live-presentation of code and software
and seminar-like form of teaching.
LERNZIELE
Discipline-specific competences:After the successful completion of this module:
∙ the students have become familiar with basic 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) in order to identify and quantify
peptides or proteins, respectively,
∙ they understand the underlying algorithmic and statistical concepts of these methods,
∙ they are able to use proteomics-specific software and the workflow engine KNIME,
∙ they are able to design and program own solution strategies
∙ and they are able to apply the discussed software tools and methods to real data and
problems.
Interdisciplinary/generic competences:
∙ instrumental competences:
– Intensive usage of the learning platform Moodle
∙ systemic competences:
– Independent learning and working
– Teamwork and ability to work in a team
∙ communicative competences:
– Presentation of own work and results
– Communication of bioinformatics-specific technical terms
– Rhetoric and linguistic competence (English)
INHALT
Inhalt:∙ Basics of protein biochemistry
∙ Properties of amino acids
∙ Basics of mass spectrometry
∙ Raw data processing
∙ Protein databases
∙ Tryptic and in silico digest of proteins
∙ Principles of spectra identification search engines and scores
∙ Limitation of the false discovery rate using the target-decoy-approach
∙ PSM-specific score-correction (Percolator)
∙ Protein inference
∙ Protein quantification
∙ Preprocessing of quantitative data
∙ Quality control
∙ Software tools used in bioinformatics for proteomics (Tutorial)
∙ Practical (programming) tasks (Tutorial)
∙ Workflow engine KNIME (Tutorial)
VORAUSSETZUNGEN CREDITS
Bestandene schriftliche oder mündliche Prüfung
EMPFOHLENE VORKENNTNISSE
Recommended prior knowledge: English and basic programmingskills.