| NUMMER: | 201911 |
| KÜRZEL: | BioInfPro2 |
| MODULBEAUFTRAGTE:R: | PD Dr. rer. medic. Martin Eisenacher |
| DOZENT:IN: | |
| FAKULTÄT: | Medizinisches Proteom Center |
| SPRACHE: | Deutsch |
| SWS: | 3 SWS |
| CREDITS: | 5 CP |
| ANGEBOTEN IM: | jedes Sommersemester |
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 the most important knowledge from the lecture
“Bioinformatics for Proteomics I” as a brief recapitulation,
∙ they have become familiar with the principles of advanced methods used in bioinformatics
for proteomics,
∙ they are able to explain and use advanced methods that currently are employed to
analyze raw data (i.e., mass spectra) and results (i.e., peptide/protein identification
and quantification results) and to interpret them biologically,
∙ 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
∙ Brief recapitulation of “Bioinformatics for Proteomics I”∙ Computational comparison of protein lists
∙ Statistics for the comparison of experimental groups
∙ Machine learning-based biomarker discovery (supervised and unsupervised methods)
∙ Enrichment analysis
∙ Network analysis
∙ Single / multiple / parallel reaction monitoring (SRM / MRM / PRM)
∙ Data independent acquisition (DIA)
∙ Algorithms for de novo sequencing of peptides
∙ Open searches
∙ Dark matter of proteomics
∙ Proteoforms
∙ Metaproteomics and proteogenomics
∙ 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, basic programmingskills and lecture/tutorials “Bioinformatics for Proteomics I” in the winter term (recommended,
not required).