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580007 VU VU Introduction to mass spectrometry data analysis (2022S)
1.00 ECTS (1.00 SWS), SPL 58 - Doktoratsstudium Pharmazie, Ernährungswissenschaften und Sportwissensch
Prüfungsimmanente Lehrveranstaltung
Labels
GEMISCHT
An/Abmeldung
Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").
- Anmeldung von Do 03.03.2022 15:00 bis Fr 20.05.2022 12:00
- Abmeldung bis Fr 20.05.2022 12:00
Details
Sprache: Deutsch
Lehrende
Termine
Lecture Dates: "Course is held online via Zoom"
23.05.2022: 09.00-11.30h24.05.2022: 09.00-11.30h25.05.2022: 12.00-14.30h30.05.2022: 09.00-11.30h31.05.2022: 09.00-11.30h01.06.2022: 09.00-11.30h02.06.2022:09.00-11.30hInformation
Ziele, Inhalte und Methode der Lehrveranstaltung
This course endows an introduction to the crucial notions and approaches in the analysis of MS datasets. Prominence will be shed on development of efficient, straight forward and universal algorithms and techniques used in common MS datasets interpretation tools. The aim of the course is to coach the students how to use algorithms and bioinformatical tools to address various problems confronted when perusing studies/carrier in the field of MS oriented analytical chemistry. The course includes case studies in the fields of LC-MS analysis and small molecules analyses (OMICS).
Art der Leistungskontrolle und erlaubte Hilfsmittel
• Understand fundamental concepts in bioinformatics
• Develop an outline of the key procedures and tools that are used in MS data analysis
• Be able to develop/implement algorithms to solve problems
• Be capable of transforming raw data into meaningful “statically relevant” data through self-developed algorithms and automation (Case studies)
• Develop an outline of the key procedures and tools that are used in MS data analysis
• Be able to develop/implement algorithms to solve problems
• Be capable of transforming raw data into meaningful “statically relevant” data through self-developed algorithms and automation (Case studies)
Mindestanforderungen und Beurteilungsmaßstab
1. Final practical assignment (to develop an algorithm and use it to analyze a dataset)
2. Homework assignments
2. Homework assignments
Prüfungsstoff
Literatur
Zuordnung im Vorlesungsverzeichnis
Letzte Änderung: Mo 25.04.2022 11:50