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

Language of instruction : English
Credits: 3,0
  
Period: quarter 3 (3sp)
  
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
    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.


Organisational and teaching methods
Organisational methods  
Lecture ✔  
Project ✔  
Small group session ✔  


Evaluation

Quarter 3 (3,00sp)

Evaluation method
Written evaluation during teaching period35 %
Homework✔
Written exam35 %
Take-home assignment✔
Oral exam30 %
Open questions✔
Evaluation conditions (participation and/or pass)✔
Conditions The student must have submitted the homeworks in time.
Consequences If the homeworks are not submitted or not submitted in time, the student will fail for the course.

Second examination period

Evaluation second examination opportunity different from first examination opprt
No
 

Compulsory course material
 

All compulsory material will be made available on Blackboard


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.

     
  •  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 in an application context, thus contributing to scientific research 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.

  •  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.

 
  EC = learning outcomes      DC = partial outcomes      BC = evaluation criteria  
Offered inTolerance3
2nd year Master Bioinformatics N
2nd year Master Bioinformatics - icp N
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.