De elektronische studiegids voor het academiejaar 2026 - 2027 is onder voorbehoud.





Analysis of Sequencing Data (3766)
Coordinating lecturer:Prof. dr. Jurgen CLAESEN 

Credits: 3,0
Study load hours: 81
Period: semester 1 (3sp)

Language of instruction: English
Exam contract: not possible

2nd Chance Exam1: Yes
Final grade2: Numerical
Tolerance3: See included in these programmes
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
    Generalized Linear Models (5463) 6.0 stptn  
    Linear Models (3560) 5.0 stptn  
    Medical and Molecular Biology (3564) 6.0 stptn  
 


Prerequisites

The student has knowledge about (generalized) linear models and molecular biology.


Content

The course focuses on the analysis of different types of sequencing experiments, such as methylation sequencing, mRNA sequencing, ChIP sequencing and ATAC sequencing. Every class consists out of a lecture and a hands-on session.


Compulsory course material
 

All compulsory material will be made available on Blackboard


Evaluation

Second examination period

Evaluation second examination opportunity different from first examination opprt
No


Learning outcomes
  EC = learning outcomes      DC = partial outcomes      BC = evaluation criteria  
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.

     
  •  DC 

    • ... correctly using state-of-the-art analysis methodology.

     
  •  DC 

    • ... correctly using state-of-the-art software.

  •  EC 

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

  •  EC 

    • The student can put research and consulting aspects of one or more statistical fields into practice.

  •  EC 

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

  •  EC 

    • The student is able to correctly use the theory, either methodologically or in an application context or both, thus contributing to scientific research within the field of statistical science, data science, or within the field of application.

     
  •  DC 

    • The student is able to correctly use the theory methodologically, thus contributing to scientific research within the field of application.

     
  •  DC 

    • The student is able to correctly use the theory in an application context, thus contributing to scientific research within the field of application.

  •  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 routinely monitors his/her own learning process and adjusts and improves it accordingly.

 
Included in these programmesTolerance3
2nd year Master Bioinformatics N
2nd year Master Bioinformatics - icp N
Exchange Programme Statistics Y



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.