Bioinformatics for Proteomics I

NUMMER: 201911
KÜRZEL: BioInfPro1
MODULBEAUFTRAGTE:R: PD Dr. rer. medic. Martin Eisenacher
DOZENT:IN: PD Dr. Martin Eisenacher
FAKULTÄT: Medizinisches Proteom Center
SPRACHE: Englisch
SWS: 3 SWS
CREDITS: 5 CP
WORKLOAD: 150 h
ANGEBOTEN IM: jedes Wintersemester

BESTANDTEILE UND VERANSTALTUNGSART

a) Vorlesung Bioinformatics for Proteomics
I (201911) - 2 SWS
b) Übung - 1 SWS

PRÜFUNGEN

FORM: schriftlich oder mündlich
ANMELDUNG:
DATUM: 0000-00-00
BEGINN: 00:00:00
DAUER:
RAUM:

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

Keine

VORAUSSETZUNGEN CREDITS

Bestandene schriftliche oder mündliche Prüfung

EMPFOHLENE VORKENNTNISSE

Recommended prior knowledge: English and basic programming
skills.

LITERATUR

AKTUELLE INFORMATIONEN

SONSTIGE INFORMATIONEN

Weitere Informationen unter http://www.bioinf.rub.de/msc/