Proteomics and Metabolomics

Course manual 2021/2022

Course content

“Omics” technologies have propelled the rate at which biomedical sciences are able to address research questions at a system wide level. Mass spectrometry has played a pivotal role in propelling quantitative protein and metabolite measurements into the realm of proteomes and metabolomes, for addressing questions related to biology or health and disease.  Proteomic and Metabolomic analyses yield large data-sets, interpretation of such data requires expertise in spectral data-analysis as well as advanced statistics and bioinformatics. It is of great importance for biomedical students to gain experience with proteome-wide, and metabolome-wide, data analysis, and experience how it can be used to answer biomedical research questions. That is why we offer a course in which we cover many aspects of the data analysis process, based on case studies from scientific literature. This course is part of the Big Biomedical Data Analysis Major program.

Study materials

Practical training material

• The syllabus / practical manual. The syllabus, practical manual, lectures and protocols will be published on Canvas or handed out.

Objectives

• The student can demonstrate an overview of both the field of metabolomics and proteomics and their methods of study.
• The student is able to explain how different mass spectrometers measure masses of molecules.
• The student is able to interpret a mass spectrum manually to assign peptide or metabolite identities.
• The student can explain how mass spectral data is translated into protein and metabolite identifications by computer search algorithms.
• The student can explain (statistical) concepts underlying selected methods and tools used for proteomics and metabolomics data analysis.
• The student is able to use proteomics and metabolomics tools to analyse mass spectral data.
• The student is able to choose appropriate statistics (multivariate, univariate) and apply them to analyse 'omics' datasets.
• The student is able to answer a biomedical research question by integrating multiple 'omics' datasets.

Teaching methods

• Lecture
• Computer lab session/practical training
• Self-study
• Working independently on e.g. a project or thesis
• Supervision/feedback meeting
• Presentation/symposium

Learning activities

Attendance

Requirements of the programme concerning attendance (OER-B):

1. Attendance during practical components exercises is mandatory.

Assessment

Inspection of assessed work

Students who wish to inspect their assessed work, can indicate this (by email) after receiving their final grade (total course) to the course coordinator. The course coordinator will then arrange a date when students can inspect their assessed work.

Assignments

Assignments (graded and non graded) and descriptions can be found on canvas. Feedback is given through canvas as wel.

Fraud and plagiarism

The 'Regulations governing fraud and plagiarism for UvA students' applies to this course. This will be monitored carefully. Upon suspicion of fraud or plagiarism the Examinations Board of the programme will be informed. For the 'Regulations governing fraud and plagiarism for UvA students' see: www.student.uva.nl

Course structure

Timetable

The schedule for this course is published on DataNose.

Honours information

NA

Additional information

This course has a Canvas site.

Contact information

Coordinator

• Gertjan Kramer

Staff

• H.L. Dekker
• dr. M.J. Jonker
• Winfried Roseboom
• dr. ir. R.C. Schuurink
• dr. D. Speijer
• B.N. Swarge PhD