Bioinformatics

Teacher(s)

Branders Vincent (compensates Dupont Pierre); Dupont Pierre;

Language

English
> French-friendly

Prerequisites

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).

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

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).

Online resources

moodle.uclouvain.be/course/view.php?id=1839
https://inginious.info.ucl.ac.be/course/LGBIO2010

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.

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

GBIO