Duration
25h Th, 15h Pr, 35h Proj.
Number of credits
Lecturer
Language(s) of instruction
English language
Organisation and examination
Teaching in the second semester
Schedule
Units courses prerequisite and corequisite
Prerequisite or corequisite units are presented within each program
Learning unit contents
This course strengthens Scientific Problem Solving by training students to select, implement, and critically evaluate computational methods for complex genomic analyses. Through collaborative project work, it develops Interdisciplinary Teamwork and promotes Ethical & Sustainable Design through rigorous, reproducible, and responsible data analysis.
This learning unit focuses on the conceptual and methodological connections between statistical and biological interactions in genetic data. Students follow a method-focused, literature-informed learning trajectory, supported by periodic face-to-face sessions that provide the theoretical and practical foundations for the analysis. The course content can be adapted dynamically in response to student interests and methodological directions.
The theoretical scaffolding is partly provided together with the student from the companion course "A tour in genetic epidemiology," but here the emphasis is on computational analysis, model choice, and the interpretation of interaction signals in genomic data. When real data are used, they serve as a benchmark for comparing the selected method with alternative approaches. When real data are not used, the course is explicitly literature-driven: students delve into the methodological literature to understand the selected analytical method as thoroughly as possible, including its assumptions, strengths, limitations, niches of applicability, and performance in this and other contexts.
Learning outcomes of the learning unit
This course is part of an integrated learning pathway in Computational and Systems Genomics. The pathway follows a deliberate progression: students first understand the data (GBIO0002), then understand the scientific questions and evidence (GBIO0015), next develop and evaluate computational methods (GBIO0030), and finally apply these competencies in an authentic research project (GBIO0031). This particular course translates conceptual understanding into the development, implementation, and critical evaluation of computational methods for genomic data analysis, preparing students for independent methodological decision-making.
The following aspects of the analysis pipeline constitute the learning outcomes of the unit:
- data preprocessing and quality control;
- selection of an appropriate computational tool and correct implementation of it;
- understanding of the advantages and limitations of the selected analytical approach;
- interpretation of interaction signals in their statistical and biological context;
- ability to discuss how analytical choices influence the robustness and meaning of the results.
Prerequisite knowledge and skills
A background in biostatistics, (bio)informatics, or statistical genetics is a plus. Alternatively, one of the following courses should have been/ are taken: GBIO0002 or GBIO0015
Planned learning activities and teaching methods
Approximately 4 theoretical sessions are organized, during which the general aspects related to method selection, interaction modeling, and critical interpretation are explained. In-between sessions may be organized to help students with reading, comparison of methods, or practical work upon request.
Collaborative Learning is the primary pedagogical mode, with Project-Based Learning as a secondary support structure. Students work in pairs or small groups on a shared methodological theme, with periodic face-to-face sessions used to provide theoretical background and to guide methodological decisions.
The course focuses on epistasis modelling and on understanding how different analytical approaches capture interaction signals in genetic data. Depending on the available setup, students may either work with real data or compare methods more theoretically. In all cases, the emphasis is on selecting, and implementing where relevant, a method critically, and on interpreting the resulting signals in their statistical and biological context.
The theoretical content may cover biological interactions, statistical interactions, and how to bridge the gap between the two, while maintaining an explicit focus on computational implementation, comparison of methods, and result interpretation.
Mode of delivery (face to face, distance learning, hybrid learning)
Blended learning
Further information:
Face to face or online, depending on the situation and organizational constraints.
Course materials and recommended or required readings
Platform(s) used for course materials:
- eCampus
Further information:
There is no mandatory textbook. Useful references will be provided as the course progresses. All course material is posted on the course website and/or eCampus.
Exam(s) in session
Any session
- In-person
oral exam
Further information:
In the first examination session, assessment is based on the individual report resulting from the project work and an individual oral examination. The assessment evaluates the student's understanding of the selected computational approach, its correct application where applicable, its assumptions, strengths and limitations, and the ability to critically interpret and compare analytical approaches in the context of statistical and biological interactions.
Because project work may be carried out collaboratively, the oral examination is used to assess each student's individual understanding and ability to justify methodological choices and critically discuss the work.
Evaluation criteria are:
- Clarity and structure of the work presented, including the report and supporting material.
- Correctness and accuracy of the analysis and interpretation.
- Appropriate choice and justification of the computational approach.
- Understanding of the assumptions, strengths and limitations of the selected approach.
- Critical interpretation of the results and ability to relate statistical interaction signals to their potential biological meaning.
- Originality, critical reflection and appropriate use of the methodological literature and theoretical course material.
- Individual understanding and ability to justify methodological choices and discuss alternative analytical strategies, as assessed during the oral examination.
In the second examination session, students are not required to repeat the project or reanalyse the data. The examination consists of an individual oral examination based on the original project/report and the methodological and theoretical content of the course. Students are expected to identify and critically discuss weaknesses in their original approach, explain how these could be addressed, and reason about appropriate alternative analytical strategies. The examination may therefore include questions that extend beyond the specific method used in the original project.
The individual oral examination determines the course grade for the second examination session. The original project/report serves as examination material and is not reassessed as a separate component.
Work placement(s)
Organisational remarks and main changes to the course
Course language: English
The course is organized in the second semester. The detailed calendar and announcements are available on the course website. Depending on the number of students enrolled, the content and practical organization of group work may be adapted to maximize the experience in a multidisciplinary environment.
Contacts
Kristel Van Steen - e-mail kristel.vansteen@ulg.ac.be
Assistant: to be communicated
Preferred contact mode: e-mail (include GBIO0030 in the subject title) or personal contact, after a lecture or by appointment.