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.
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
Q1
Teacher(s)
Dupont Pierre;
Language
English
Prerequisites
Students are expected to master the following skills :
The following skills are also useful. They are briefly reviewed at the beginning of the LGBIO2010 course :
- 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,
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,
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
Aims
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 :
- 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”.
Content
- 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.
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
The final exam is, by default, a written exam (on paper or, when appropriate, on a UCLouvain computer).
- 25% for computing projects implemented in groups during the semester
- 75% for the final exam
The final exam is, by default, a written exam (on paper or, when appropriate, on a UCLouvain computer).
Online resources
Bibliography
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.
- 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
GBIO
Programmes / formations proposant cette unité d'enseignement (UE)
Title of the programme
Sigle
Credits
Prerequisites
Aims
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