Duration
30h Th, 15h Pr, 15h Proj.
Number of credits
Lecturer
Coordinator
Language(s) of instruction
English language
Organisation and examination
Teaching in the first semester, review in January
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 model, analyse, and critically interpret complex omics data. Through collaborative case studies, it also develops Interdisciplinary Teamwork and promotes Ethical & Sustainable Design by fostering reproducible, responsible, and transparent bioinformatics practices.
This learning unit introduces core concepts in genetics, bioinformatics, and analytical modelling through a combination of traditional teaching, Case-Based Learning (CBL), and Collaborative Learning (CL). Students learn how biological data are generated, processed, analysed, and interpreted across major omics domains.
Topics include genomics, transcriptomics, proteomics, metabolomics, microbiomics, data generation technologies, first-line analytical processing (quality control, alignment, quantification, normalization, filtering, annotation), bioinformatics workflows, reproducibility, genome-wide association studies (GWAS), network analysis, transcriptomic and microbiome analyses, metabolic networks, multi-omics integration, and translation of bioinformatics findings to biomedical applications.
A GWAS serves as the central case throughout the course. Using guided scientific papers, structured questions, and collaborative activities, students progressively connect biological questions, analytical modelling, statistical considerations, and biological interpretation.
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 provides the analytical and biological foundations on which the subsequent courses build, introducing students to omics technologies, bioinformatics workflows, and the interpretation of complex biological data.
All students
- Must have acquired a foundational understanding of molecular biology and genetics, enabling them to ground bioinformatics analyses in biological concepts.
- Must be familiar with the key technological principles of DNA sequencing and specific cutting-edge cell and molecular biology technologies, and must be able to recommend their application in specific biological contexts.
- Explain the principles of major omics data types and their generation technologies.
- Describe the main steps of bioinformatics workflows, including GWAS quality control, statistical testing, and biological interpretation.
- Identify common analytical issues such as normalization, bias, population structure, multiple testing, reproducibility, and interpretation limits.
- Connect biological data generation with downstream analytical modelling.
- Critically evaluate published bioinformatics analyses and their limitations.
- Collaborate effectively to analyse scientific cases and communicate concise scientific conclusions.
Prerequisite knowledge and skills
A background in genetics, bioinformatics, biostatistics, biomedical engineering, or computer science is helpful but not required. Students should be able to read scientific literature, participate actively in group discussions, and engage in guided analytical reflection.
Planned learning activities and teaching methods
The course combines lectures with structured Case-Based Learning and Collaborative Learning. Short teaching sessions introduce each topic before students work in groups on guided cases based on scientific papers or prepared computational exercises.
Across all thematic sessions, students identify the biological question, discuss the analytical workflow, evaluate statistical and biological considerations, and prepare a concise group output (poster, slide, or flowchart). The GWAS case provides a recurring example that links multiple course topics and supports progressive integration of concepts.
The instructor provides structured guidance, selected resources, and facilitation, ensuring that students with diverse backgrounds can actively engage with modern bioinformatics analyses.
Part I (data and technologies)
Teaching focuses on data types (genomics, transcriptomics, proteomics, metabolomics), the platforms that generate these data, and the first-line ("low-level") analytics required to transform raw measurements into analyzable data. These steps include quality control, alignment, quantification, normalization, filtering, and annotation, depending on the omics type. While Part I remains lecture-based this year, students are expected to actively engage with examples and conceptual workflows, which prepare them for the more applied elements of Part II.
Part II (high-level analytics)
Teaching is structured around recurring elements in each domain session to provide consistency across topics and support a heterogeneous student population:
- Conceptual framing - What is study X about and why do we perform it (e.g., GWAS, differential gene expression, metabolomic maps)? What kind of biological questions can be answered, and what are the assumptions and pitfalls?
- Methods overview - Introduction to typical analytical pipelines, common tools, and links to translational applications such as disease interpretation or drug development.
- Group work and output - Students work on either (a) guided computational exercises (e.g., pre-written RMarkdown scripts) or (b) problem-based learning activities (e.g., reverse-engineering a published analysis). Groups then produce a short output (poster, slide, or flowchart) summarizing the biological question, analytical strategy, results/visualization, and critical reflections, followed by peer exchange and feedback.
Mode of delivery (face to face, distance learning, hybrid learning)
Blended learning
Further information:
The course will be held in person. Online sessions will take place only on an exceptional basis, for example for a guest lecture or in unforeseen circumstances (e.g., COVID-19 developments). The course is structured into two consecutive blocks (Part I and Part II), covering elements of Genetics and Bioinformatics.
Course materials and recommended or required readings
Platform(s) used for course materials:
- eCampus
Further information:
There is no single text book that covers all aspects of the course. Course note materials (slides and supporting documentation as reference) will be provided during the course.
Exam(s) in session
Any session
- In-person
written exam ( multiple-choice questionnaire, open-ended questions )
Further information:
Assessment is based on participation in guided learning activities and the quality of group deliverables, such as summary slides, posters, or brief syntheses. Student performance will be evaluated primarily through a written exam, consisting of open questions and multiple-choice questions (QCM). This evaluation focuses on conceptual understanding, clarity of communication, appropriate interpretation of analytical and biological results, recognition of methodological limitations, and effective collaboration.
In response to student feedback from previous years, credit will be awarded for work completed at home or through group assignments. In particular, a minimum of 1 point (out of 20) can be earned for active participation in group work or by completing the R-based homework exercises. This bonus credit cannot be carried over to second trial examinations.
Work placement(s)
Organisational remarks and main changes to the course
The course will be given in English / French
The course is organized in the first quadrimestre. The detailed calendar and announcements are available on the course website.
Contacts
Teaching staff and contact details
-
Kristel Van Steen - kristel.vansteen@uliege.be
Responsible for the overall course and Part II (Bioinformatics - high-level analytics)
-
Franck Dequiedt - fdequiedt@uliege.be
Responsible for Part I (Genetics - data and technologies)
Association of one or more MOOCs
There is no MOOC associated with this course.