Analysis of Protein Expression (1757)
  
Coordinating lecturer :Prof. dr. Dirk VALKENBORG 
  
Co-lecturer :Prof. dr. Tomasz BURZYKOWSKI 

Language of instruction : English
Credits: 4,0
  
Period: semester 1 (4sp)
  
2nd Chance Exam1: Yes
  
Final grade2: Numerical
 
Exam contract: not possible
 
Sequentiality
 
   Mandatory sequentiality bound on the level of programme components
 
 
  Following programme components must have been included in your study programme in a previous education period
    Medical and Molecular Biology (3564) 6.0 stptn
 

Prerequisites

The student has knowledge about molecular biology and understands the basic concepts of chemistry, physics, computer programming, combinatorics, statistics, biochemistry and biosynthesis.


Content

Bioinformatics; protein identification; protein expression analysis; mass spectrometry.

The course focuses on problems related to the analysis of protein sequences and protein expression. Examples of topics: protein identification using tandem mass spectrometry by database searches; analysis of protein expression data obtained by using mass spectrometry; diagnostic classification using protein mass spectra, isotope computation and low-level preprocessing of mass spectral data, quantitative proteomics by isobaric labels, isotope modeling, wavelet analysis for peak detection and noise removal.

Student possesses a general knowledge about topics and problems related to the analysis of protein sequences and protein expression. S/he is familiar with issues, methods and software associated with spectral interpretation. S/he is familiar with various experimental techniques used to study protein expression levels. S/he knows basic problems and methods related to the analysis of data obtained by using the techniques.


Organisational and teaching methods
Organisational methods  
Excursion/Fieldwork ✔  
Lecture ✔  
Project ✔  
Teaching methods  
Discussion/debate ✔  
Homework ✔  
Presentation ✔  


Evaluation

Semester 1 (4,00sp)

Evaluation method
Written evaluation during teaching period20 %
Transfer of partial marks within the academic year✔
Report✔
Written exam20 %
Report✔
Oral exam60 %
Open questions✔
Presentation✔
Evaluation conditions (participation and/or pass)✔
Conditions A student must at least attend all components of the evaluation. All projects should be submitted before the deadline.
Consequences Students get an X score if they do not meet the conditions.

Second examination period

Evaluation second examination opportunity different from first examination opprt
No
 

Compulsory course material
 

Lecture notes; photo/electronic copies of relevant book chapters and papers; lab visit

 

Recommended reading
  Principles of Proteomics,Richard Twyman,2,Garland Science,9780815344728,Available as e-book: https://www.taylorfrancis.com/books/mono/10.1201/9780429258527/principle s-proteomics-ph-cfe-richard-twyman-george

Computational Methods for Mass Spectrometry Proteomics,Ingvar Eidhammer; Kristian Flikka; Lennart Martens; Svein-Ole Mikalsen,Wiley,9780470512975,Available as e-book: https://onlinelibrary-wiley-com.bib-proxy.uhasselt.be/doi/book/10.1002/9 780470724309

Protein Bioinformatics: An Algorithmic Approach to Sequence and Structure Analysis,Ingvar Eidhammer; Inge Jonassen; William R. Taylor,Wiley,9780470848395,Available as e-book: https://onlinelibrary-wiley-com.bib-proxy.uhasselt.be/doi/book/10.1002/9 780470092620


Learning outcomes
Master of Statistics and Data Science
  •  EC 

    • The student can handle scientific quantitative research questions, independently, effectively, creatively, and correctly using state-of-the-art design and analysis methodology and software.

  •  EC 

    • The student can critically appraise methodology and challenge proposals for and reported results of data analysis.

  •  EC 

    • The student can work in a multidisciplinary, intercultural, and international team.

  •  EC 

    • The student is able to efficiently acquire, store and process data.

  •  EC 

    • The student is an effective written and oral communicator, both within their own field as well as across disciplines.

  •  EC 

    • The student is capable of acquiring new knowledge.

  •  EC 

    • The student knows the international nature of the field of statistical science and data science.

 
  EC = learning outcomes      DC = partial outcomes      BC = evaluation criteria  
Offered inTolerance3
2nd year Master Bioinformatics J
2nd year Master Bioinformatics - icp J
2nd year Master Biostatistics J
2nd year Master Data Science J
2nd year Master Quantitative Epidemiology J
Exchange Programme Statistics J



1   Education, Examination and Legal Position Regulations art.12.2, section 2.
2   Education, Examination and Legal Position Regulations art.15.1, section 3.
3   Education, Examination and Legal Position Regulations art.16.9, section 2.