Bioinformatics : DNA and protein sequences

lbrmc2201  2021-2022  Louvain-la-Neuve

Bioinformatics : DNA and protein sequences
4.00 credits
30.0 h + 15.0 h
Q1
Teacher(s)
Language
French
Prerequisites
Introductory courses in biochemistry and molecular biology
Main themes
Bioinformatics refers to a set of concepts and tools that are required for the analysis of biological data and the interpretation of the results. This introductory course focuses on molecular biology databases (DNA and protein sequences), the algorithmic bases of the sequence analysis programs and on alignment score statistics. The course identifies the many pitfalls of interpreting data by giving a critical appraisal of the softwares used for sequence analysis.
Learning outcomes

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

1 a. Contribution de l'activité au référentiel AA (AA du programme)
Cohérence des AA cours en regard de ceux du programme
1.1, 1.2, 1.3
3.1, 3.2, 3.4, 3.5, 3.6
b. Formulation spécifique pour cette activité des AA du programme (maximum 10)
At the end of this course, students will be able to perform a comprehensive and exhaustive sequence analysis, using appropriate computational programs tools and internet resources. This ability requires:
- The understanding of the algorithmic bases of the computational programs
- The description of the various molecular databases with emphasis on the positive and negative aspects of data structure and search tools
- The discussion of the prediction results and eventually the proposition of a more appropriate analysis method
-A strategy for protein function forecasting
 
Content
1.     Introduction : Overview of bioinformatics concepts
2.     Sequence and 3-D structure databases, protein motif and family databases
3.     Sequence comparison : dot plot, global and local alignment based on a dynamic programming method and score matrices
4.     Database searching for similar sequences (matching word-based method), score statistics 
5.     Multiple sequence alignment, motif discovery,(patterns, profiles, Hidden Markov models)
6.     Phylogenetic inference using phenetic and cladistic methods
7.    High-throughput analysis of gene expression: RNA-seq
Teaching methods
The theoretical part consists of ex cathedra speeches in a classroom (30h).  The training sessions (15h) consist of a set of problems to be resolved individually or by a group of 2 students, using free sequence analysis programs.
Evaluation methods
Criteria including (i) the understanding of the algorithmic bases of the sequence analysis programs, (ii) the use of the most appropriate program and database, and (iii) the explanation of the statistical bases of prediction scores will be assessed via a written examination in an open-book format (60% of the final mark). 
Practical training is assessed during the quadrimester, via problems to solve individually or by a group of 2-3 students (40% of the final mark)
Other information
This course can be given in English.
Online resources
Moodle
Bibliography
Des copies papier des diaporamas et le manuel pour les exercices sont disponibles sur Moodle.
Le cours ne fait appel à aucun support particulier qui serait payant et jugé obligatoire. Les ouvrages Bioinformatics de Mount (CSHL press) et Bioinformatics and functional genomics de Pevsner sont conseillés pour un apprentissage plus approfondi
Teaching materials
  • Bioinformatics sequence and genome analysis D. Mount
  • Bioinformatics and functional genomics by Pevsner
Faculty or entity


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

Title of the programme
Sigle
Credits
Prerequisites
Learning outcomes
Master [120] in Statistics: Biostatistics

Master [120] in Chemistry and Bioindustries

Master [120] in Biochemistry and Molecular and Cell Biology

Master [60] in Biology