lgbio2010  2019-2020  Louvain-la-Neuve

Note from June 29, 2020
Although we do not yet know how long the social distancing related to the Covid-19 pandemic will last, and regardless of the changes that had to be made in the evaluation of the June 2020 session in relation to what is provided for in this learning unit description, new learnig unit evaluation methods may still be adopted by the teachers; details of these methods have been - or will be - communicated to the students by the teachers, as soon as possible.
5 credits
30.0 h + 30.0 h
Dupont Pierre;
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

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).

The contribution of this Teaching Unit to the development and command of the skills and learning outcomes of the programme(s) can be accessed at the end of this sheet, in the section entitled “Programmes/courses offering this Teaching Unit”.
  • 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
  • Survival data analysis
Teaching methods
Lectures and computing projects.
The projects are made in groups of 2 students to implement, possibly to adapt, concrete algorythms covered in the course lectures.
The R language is recommended to implement these projects. An R tutorial is included at the beginning of the first project.
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 UCLouvain 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 on 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 sur le site Moodle du cours.
Faculty or entity

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

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

Master [120] in Biomedical Engineering

Master [120] in Computer Science and Engineering

Master [120] in Mathematical Engineering

Master [120] in Statistic: Biostatistics

Master [120] in Computer Science

Master [120] in Data Science: Information Technology