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.
3 credits
15.0 h
Q2
Teacher(s)
Bugli Céline; Govaerts Bernadette;
Language
French
Content
After reviewing the basics of molecular biology, the course presents a series of -omics methods and especially related data processing methods:
- Molecular biology basics.
- Revision of multivariate methods useful in -omics methods (PCA, Clustering...) and application in R + RMarkdown.
- Transcriptomic data acquisition method (micro-arrays, q-PCR...).
- Pretreatment and analysis of transcriptomic data (background correction, normalization,.... + hypothesis tests with multiplicity correction).
- Use of prediction and classification models from chemometry and machine learning for the analysis of omic data (PLS, O-PLS, trees...).
- Acquisition and processing of proteomic data.
- Acquisition and processing of metabolomic data (including detailed pre-processing of 1H-NMR data).
- Processing of metagenomic data.
Teaching methods
The course consists of a series of activities that lead the student to actively immerse himself in the world of -omics data. It proposes:
- presentations by specialists active in the field,
- mini-projects of data processing to be carried out each week,
- interactive computer work during the course,
- a laboratory visit,
- a final project on data proposed by the various participants in the course or data repositories.
Evaluation methods
The evaluation is based on:
- Small projects proposed after each course,
- a final project and a linked oral presentation,
- an oral exam (with open documentation).
Online resources
Moodle Site: https://moodleucl.uclouvain.be/course/view.php?id=10846
Faculty or entity
LSBA
