Duration
9h Th, 9h Pr
Number of credits
| Master in agricultural bioengineering (120 ECTS) | 2 crédits |
Lecturer
Coordinator
Language(s) of instruction
French language
Organisation and examination
Teaching in the first semester, review in January
Schedule
Units courses prerequisite and corequisite
Prerequisite or corequisite units are presented within each program
Learning unit contents
Languages
French and English
Content
With the exponential growth in generated data in life sciences, the bioengineer must be able to tame and understand bioinformatics and its importance in research for the future. The objective of this course is to introduce students to bioinformatics through the approach "learning by doing".
In the first part, they must set up and use a pipeline for analyzing high-throughput sequencing data in order to study and compare the microbial communities present in different samples. Here is an introductory tutorial on this topic: https://www.youtube.com/watch?v=6564K4-_DBI. After a theoretical introduction (1.5 hour), the students receive high-throughput sequencing data generated from microbial communities as well as the objectives (biological question) pursued for the data analysis. The whole course, oriented towards the development of a project, will be centered on the selection, application and documentation of a bioinformatics pipeline using QIIME2 following a step-by-sep tutorial freely accessible online and used by a large community of biologists. Coaching throughout the project and video tutorials will be available to guide the students.
In the second part, the students will learn how to perform high throughput RNA sequencing, what is the significance of differential expression and how to use GO enrichment tools to find biological differences between two or more experimental conditions. Furthermore, they have to work with the WGCNA package themselves using the online tutorial: https://horvath.genetics.ucla.edu/html/CoexpressionNetwork/Rpackages/WGCNA/Tutorials/. As in the first part, a theoretical introduction is followed by hands-on training with RNA sequencing data. The software to be used will be R studio.
Learning outcomes of the learning unit
The learning outcomes are as follows:
- Ability to understand and apply a bioinformatics analysis of high-throughput sequencing data according to the biological questions asked (transpose a biological question into bioinformatic analysis using the appropriate tools)
- Ability to critically analyze a bioinformatics pipeline
- Ability to extract important biological information from raw data
- Develop the capacity for qualitative and quantitative interpretation of "big data"
Prerequisite knowledge and skills
A good knowledge of molecular biology and associated techniques (PCR, sequencing, ...) is necessary.
Knowledge of bioinformatics or programming language is not required. The course is an introduction to bioinformatics and does not require prior knowledge on programming.
Planned learning activities and teaching methods
The course relies on a flipped classroom and learning by doing. For each part, a general introduction will be given ex-cathedra for 1.5 hour. This introduction will explain the context, objectives, learning methods and evaluation of this course. The raw data to be analyzed will also be transferred to the students
Online tutorials will then be made available to students and must be followed. These tutorials explain the implementation of the bioinformatics analysis pipeline as well as the usefulness of the various bioinformatics analyzes to be carried out.
On the basis of these tutorials and personal research, the students will set up their bioinformatics analysis pipeline allowing them to interpret the raw data received. Throughout the development, students will be coached by the teacher through regular meetings to guide developments and resolve problems encountered.
The implementation of the analysis pipeline is individual, but students are also invited to collaborate with each other (peer learning)
Mode of delivery (face to face, distance learning, hybrid learning)
Blended learning
Additional information:
For each part:
- Presential: 1.5h of introduction + regular coaching
- Distance: on-line tutorials + personal work on the dataset
Recommended or required readings
Selection of video tutorials from this channel: https://www.youtube.com/channel/UC7QH-fgE50r2mDnkm88fzMg (part 1) and online info to get started with the WGCNA package at: https://horvath.genetics.ucla.edu/html/CoexpressionNetwork/Rpackages/WGCNA (part 2).
Written work / report
Additional information:
For the first part, microbial community analyses, the evaluation will correspond to the average between the documentation of the pipeline put in place (bioinformatics documentation part) and the analysis report (translation of the results of bioinformatics analysis into information that can be used by a biologist). A pdf document must be provided for the documentation part (5 pages maximum) and the results will be presented in the form of a powerpoint (6 slides maximum).
For the second part, we ask for an analysis report on the RNA sequencing data, either provided by the teacher or on own data if present (5 pages maximum).
Work placement(s)
Organisational remarks and main changes to the course
Contacts
n.a.