Duration
20h Th, 5h Mon. WS
Number of credits
Lecturer
Language(s) of instruction
French 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
[UPDATED IN 2022] Today almost all studies involving molecular biology require a lot of data analysis (i.e., bioinformatics). Bioinformaticians act both upstream and downstream of molecular biologists, for example to propose a statistically sound experimental design and to generate new hypotheses from high-throughput data, which are then tested in the laboratory.
This trend is mainly due to technological advances (e.g., robots), which produce ever more data for ever less money and labor. Consequently, many research projects are delayed due to the lack of suitable bioinformaticians for mining the data. It is thus critical that biologists turn to bioinformatics, especially to stay completely in control of their future research.
In this context, the aim of the bioinformatics course is to educate students about the potential of this hybrid discipline between biology and computer science. In particular, it aims to show that bioinformaticians can be "real" biologists, whose tool of choice is the computer instead of the pipette.
This course will enable students to become aware of the role of bioinformatics and the place of bioinformaticians in modern biology. It may also lead some people to look more closely at this domain, for example by choosing to switch to the Master in Bioinformatics and Modeling in block 2.
- Genomic databases and probabilistic sequence models
- Prokaryotic gene finding
- Sequence alignment
- Heuristics for sequence alignment and fragment assembly
- Hidden Markov models and eukaryotic gene finding
- Multiple sequence alignment and sequence profiles
- Variation in DNA sequences and genetic distances
- Quantification of natural selection
- Phylogenetic inference
- Comparative genomics and phylogenetic footprinting
- Gene expression analysis and motif discovery
Learning outcomes of the learning unit
- Theory: At the end of this course, students will be able to clearly explain the statistical and algorithmic bases of the analytical approaches seen in class. This requires, above all, to have UNDERSTOOD the course material. In this sense, this course is probably different from some other subjects of the curriculum in biology, in which mere memorization may be enough to pass the examination. The level of detail required when explaining the material roughly matches that of the companion textbook (see below), except when the slides shown in class (which will be available on eCampus) are more comprehensive than the book. Note that the biological "background" of the case studies presented in the book is considered an illustration of the methods and is thus not part of the material.
- Exercises: For some algorithms (specified in class), an APPLICATION using pen and paper to solve a toy example may be requested.
Prerequisite knowledge and skills
The course assumes no prerequisites in computer science, but relies on a basic knowledge of mathematics and molecular genetics. In principle, the necessary level in both subjects is reached at the end of the 3rd year of BA in biology.
Complementarities will be ensured with the Genomics course [GENE0003-1] taught in M-BBMC/M-BIM by Prof. Marc Hanikenne.
Planned learning activities and teaching methods
Lectures will be given in a classroom. They will last 2h and will consist of a blend of short ex cathedra speeches (30-45 min) and challenges to be solved in small groups of students. The subsequent sharing of group research results will allow to discuss and synthesize the concepts addressed.
Mode of delivery (face to face, distance learning, hybrid learning)
Face-to-face course
Additional information:
This is a face-to-face course (see above). Although topics are mostly taken from the reference book cited below, attending the lectures is strongly encouraged as they are designed so as to facilitate understanding and assimilation of the course material.
Course materials and recommended or required readings
This course is based on a reference book [N Cristianini and MW Hahn (2007) Introduction to Computational Genomics, Cambridge University Press]. Such a choice ensures a good coverage of bioinformatics topics (here defined as the analysis of genomes) while providing students with a solid yet readable reference. However, the purchase of this book is not required.
Moreover, at the latest after each lesson, the slides shown during the class will be made available to students through eCampus.
Finally, a collaborative syllabus has been written as a set of Google Docs. Students will be granted access to it, so that they can improve it.
Exam(s) in session
Any session
- In-person
written exam ( open-ended questions )
Additional information:
Written examination (January session) including three theory questions (free-form explanation of a theorical concept) and one exercise (algorithm to be applied). Among those four questions, two will be taken out of a collection of solved questions handed out to the students before the exam.
Work placement(s)
Organisational remarks and main changes to the course
Taking notes on a laptop or tablet is allowed. However, students are expected not to surf or chat in the classroom.
Contacts
Prof. Denis Baurain
Institut de Botanique B22 (P70)
denis.baurain@uliege.be
Mrs Rosa Gago
rgago@uliege.be