Learning from genomic data

Duration

150h Proj.

Number of credits

Lecturer

Kristel Van Steen

Language(s) of instruction

English language

Organisation and examination

Teaching in the second semester

Units courses prerequisite and corequisite

Prerequisite or corequisite units are presented within each program

Learning unit contents

Students are provided with an active research problem that requires carrying out a detailed genome-wide association (interaction) study (GWA; GWAI; GWEI). This research problem is provided by the supervisor or via ongoing collaborative efforts.  
Depending on the nature of the data students may opt for parametric or non-parametric analysis methods, statistical or machine learning based analytic tools. Any technique from previous courses is allowed, as long as it is appropriate for the problem at hand.   Given the interdisciplinary nature of the project work, students can work in groups of 2 or 3. Upon termination of the project, each individual prepares an individual report.  There are no class sessions apart from one during which the project problem is introduced. Theoretical and practical guidance is offered upon request.

Learning outcomes of the learning unit

Given data, students are able to apply and reinforce acquired knowledge on a practical problem in statistical genetics. In particular, students are able to carry out a sound and detailed genetic association interaction analysis, with the most appropriate software tool at hand, covering the following aspect from the analysis pipeline: data cleansing, statistical analysis, interpretation, reporting. 

Prerequisite knowledge and skills

A background in biostatistics, bioinformatics or statistical genetics is a pro. Alternatively, one has taken either one of the following courses: GBIO0002, GBIO0009, GBIO0030.

Planned learning activities and teaching methods

During a kick-off meeting, the problem and data are introduced. Students can use their own data upon agreement with the course responsible. Students can work alone (discouraged) or in groups of maximum three students. Several groups may work on the same or different real-life data set (depending upon availability). Supervision is provided via members of the BIO3 group at the GIGA or the EECS department of the School of Applied Sciences. Group meetings with the supervisors may be organized upon request. Upon termination of the project, each individual prepares a report and orally defends it.

Mode of delivery (face-to-face ; distance-learning)

Primarily distance learning

Recommended or required readings

There is no mandatory textbook. Essential information will be posted on the websites


http://bio3.giga.ulg.ac.be/
ou
http://www.montefiore.ulg.ac.be/~kvansteen/

Assessment methods and criteria

Final grading is based on a report and its oral defense using the following 5 criteria:

• Ability to formulate the research problem and the context (introductions, data description)
• Presentation of the analysis workflow (methods, analysis section)
• Quality of the analysis (validity of results)
• Creative input (analytic tool, stuffing, conclusion section)
• Quality of the report and presentation slides

Work placement(s)

Organizational remarks

The project work is organized in the second semester.
Exam in June
 

Contacts

Kristel Van Steen - e-mail kristel.vansteen@ulg.ac.be
Assistant: to be determined
Preferred contact mode: e-mail or personal contact, after a lecture or by appointment