lgbio2010  2021-2022  Louvain-la-Neuve

5.00 credits
30.0 h + 30.0 h
Students are expected to master the following skills :
  • implement and test a solution in the form of a software prototype and/or a numerical model,
  • demonstrate a good understanding of the basic concepts and the methodology of programming,
  • make a relevant choice between several data representations and algorithms to process them,
  • analyse a problem to provide an IT solution and implement it in a high level programming language,
  • understand and know how to apply in various stuations the basic concepts of probability and statistical inference,
  • use a scientific approach to extract reliable information from a data sample,
as covered within the courses LEPL1401, LEPL1402, LEPL1109
The following skills are also useful. They are briefly reviewed at the beginning of the LGBIO2010 course :
  • explain the functions that take place in the cells of a living organism,
  • describe the basic concepts of molecular genetics,
  • define the different classes of biomolecules and their links within the cell processes and structures,
as covered within the courses LGBIO1111 and LBIR1250A
Main themes
  • Introduction to molecular biology
  • Searching methods in biological databases
  • Sequence comparisons, sequence alignment algorithms
  • Motif search
  • Hidden Markov models
  • Gene expression measurement technology
  • Transcriptome analysis methods
  • Inference of interaction networks
  • Phylogeny
Learning outcomes

At the end of this learning unit, the student is able to :

1 With respect to the AA referring system defined for  the Master in biomedical engineering, the course contributes to the development, mastery and assessment of the following skills :
  • AA1.1, AA1.2, AA1.3
  • AA2.2, AA2.4
  • AA4.3
  • AA5.3
At the end of this course, students will be able:
 - to master the basic concepts of molecular biology for appropriate use of  bioinformatics tools,
- to design and develop tools or methods for  database management, information extraction  and data mining,
- to formulate informed decisions between the many  computational methods that are available for solving biological questions,
- to carry out a collaborative project aiming at the resolution of a bioinformatics problem and  taking benefit from complementary student's education and expertise,
- to use the information available in major sequence databases (Genbank, Uniprot) with a critical mind and with discernment,
- to master a software environment such as R (Bioconductor).
  • Overview of basic concepts in molecular biology
  • Search in biological databases
  • Sequence comparison, pairwise and multiple sequence alignments
  • Hidden Markov models
  • Phylogenetic tree inference algorithms
  • Gene expression analysis methods (transcriptomics)
  • Biomarker selection
  • Predictive modeling
Teaching methods
Lectures and computing projects.
  • The projects are made in groups of (max) 2 students to implement, possibly to adapt, concrete algorithms covered in the course lectures.
  • The projects are implemented in R. An R tutorial is included at the beginning of the first project.
Practical projects are submitted on line and evaluated on the Inginious platform.
Evaluation methods
The final grade consists of
  • 25% for computing projects implemented in groups during the semester
  • 75% for the final exam
The projects cannot be re-implemented for the second session. Hence, the project grade is fixed at the end of the semester.
The final exam is, by default, a written exam (on paper or, when appropriate, on a computer).
Recommended textbooks - Ouvrages complémentaires conseillés :
- Biological Sequence Analysis : Probabilistic Models of Proteins and Nucleic Acids, R. Durbin et al., Cambridge University Press, 1998.
- Inferring Phylogenies, J. Felsenstein, Sinauer Associates; 2nd ed., 2003.
- Bioinformatics, Sequence and Genome Analysis, D. Mount, Cold Spring Harbord Laboratory Press, 2nd ed., 2004.
- Introduction to Computational Genomics : a case-study approach, N. Cristianini M. Hand, Cambridge University Press, 2007.
Teaching materials
  • Required teaching material include all documents (lecture slides, project assignments, complements, ...) available from the Moodle website for this course.
  • Les supports obligatoires sont constitués de l'ensemble des documents (transparents des cours magistraux, énoncés des travaux pratiques, compléments, ...) disponibles depuis le site Moodle du cours.
Faculty or entity

Programmes / formations proposant cette unité d'enseignement (UE)

Title of the programme
Learning outcomes
Master [120] in Data Science Engineering

Master [120] in Computer Science and Engineering

Master [120] in Data Science: Information Technology

Master [120] in Statistics: Biostatistics

Master [120] in Biomedical Engineering

Master [120] in Computer Science

Master [120] in Mathematical Engineering