Genetics and bioinformatics

Duration

30h Th, 15h Pr, 15h Proj.

Number of credits

Lecturer

Franck Dequiedt, Kristel Van Steen

Coordinator

Kristel Van Steen

Language(s) of instruction

English language

Organisation and examination

Teaching in the first semester, review in January

Schedule

Schedule online

Units courses prerequisite and corequisite

Prerequisite or corequisite units are presented within each program

Learning unit contents

In this course, genetic and bioinformatics concepts are introduced that are necessary to understand a range of data analysis problems. To address these problems, various analytic tools and approaches are explained and exemplified. Topics typically include:

• Genomics, transcriptomics, and proteomics data types
  
  
• Data generation platforms and techniques
  
  
• Bioinformatics and data analytics (including reproducibility and professional practices)
  
  
• Genome-wide association studies (GWAS)
  
  
• Network analytics
  
  
• Microbiomics
  
  
• Transcriptomics analyses
  
  
• Metabolic networks linked to transcriptomics
  
  
• Integrative approaches to translate findings into clinical applications
  
  

Learning outcomes of the learning unit

At the end of this course, students will be able to describe and compare major types of omics data and the platforms that generate them, as well as bioinformatics approaches used to analyze such data. They will recognize the possibilities and limitations of different data types and methods, and critically reflect on the assumptions, pitfalls, and opportunities inherent to these analyses. Students will develop an informed and cautious attitude towards data analysis, enabling them to evaluate whether analyses are conducted appropriately rather than performing them in depth themselves.

This course contributes to the learning outcomes I.1, I.2, II.1, II.2, III.1, III.2, III.3, V.2, VI.1, VI.2, VII.1, VII.2, VII.3, VII.4, VII.5 of the BSc in engineering.


This course contributes to the learning outcomes I.1, I.2, II.1, II.2, III.1, III.2, III.3, V.2, VI.1, VI.2, VII.1, VII.2, VII.3, VII.4, VII.5 of the MSc in computer science and engineering.

Prerequisite knowledge and skills

A background in biomedicine is a pro, but not essential.

Planned learning activities and teaching methods

The course combines interactive lectures with structured discussion sessions to guide students through both foundational and advanced aspects of omics data analysis.

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:

Student performance will be evaluated primarily through a written exam, consisting of open questions and multiple-choice questions (QCM).
In response to student feedback from previous years, additional credit is awarded for work completed at home or through group assignments. Up to 1 point (out of 20) can be earned for active participation in group work or by completing the R-based homework exercises.

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.