Teacher(s)
Cobbaut Mathias; Stenuit Benoît (coordinator);
Language
English
> French-friendly
> French-friendly
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
- Introduction to bioinformatics and overview of key concepts
- Major biological databases for nucleotide and protein sequences, protein 3D structures, conserved motifs, protein families, and organisms
- Sequence comparison: dot plots, global and local alignments using dynamic programming and scoring matrices
- Database searches for similar sequences using word-based matching methods, hash tables, and score statistics
- Multiple sequence alignment and motif discovery (patterns, profiles, and Hidden Markov Models)
- Phylogenetic inference using phenetic and cladistic approaches
- High-throughput analysis of metagenomes and (meta-)transcriptomes (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
The final assessment for this course consists of two components:
1. a continuous summative assessment (35% of the final grade), including:
The assessment is designed to evaluate the understanding of the algorithmic principles underlying sequence analysis and prediction methods, the ability to select the most appropriate bioinformatics tools, the critical interpretation and statistical analysis of the results obtained, and the ability to apply the methods presented during the course.
The final grade is calculated as the sum of grades A, B and C, corresponding respectively to 10%, 25% and 65% of the final grade.
The questions and exercise statements are provided in English. Students may answer in either English or French.
1. a continuous summative assessment (35% of the final grade), including:
- practical exercises and assignments completed during the semester and assessed by M. Cobbaut (grade A: 10% of the final grade, corresponding to 2 marks out of 20);
- an assignment including a written report assessed by B. Stenuit (grade B: 25% of the final grade, corresponding to 5 marks out of 20).
The assessment is designed to evaluate the understanding of the algorithmic principles underlying sequence analysis and prediction methods, the ability to select the most appropriate bioinformatics tools, the critical interpretation and statistical analysis of the results obtained, and the ability to apply the methods presented during the course.
The final grade is calculated as the sum of grades A, B and C, corresponding respectively to 10%, 25% and 65% of the final grade.
The questions and exercise statements are provided in English. Students may answer in either English or French.
Other information
The use of generative artificial intelligence tools (such as ChatGPT, Claude, Consensus, Perplexity, etc.) is permitted, without requiring disclosure, for exploratory research of information, identifying leads or relevant sources, as well as for clarifying concepts and language assistance.
However, the use of these tools to develop the substantial content of reports, presentations, or any other evaluated material (drafting the argumentation, structuring ideas, generating substantial code, or translation that has not been significantly reworked) must be explicitly declared and referenced in an appendix to the work, specifying the tool used and the nature of the use.
The student remains solely responsible, in all cases, for the content and form of the submitted work, and must be able to explain, upon request, how they used artificial intelligence.
However, the use of these tools to develop the substantial content of reports, presentations, or any other evaluated material (drafting the argumentation, structuring ideas, generating substantial code, or translation that has not been significantly reworked) must be explicitly declared and referenced in an appendix to the work, specifying the tool used and the nature of the use.
The student remains solely responsible, in all cases, for the content and form of the submitted work, and must be able to explain, upon request, how they used artificial intelligence.
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
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
