Due to the COVID-19 crisis, the information below is subject to change,
in particular that concerning the teaching mode (presential, distance or in a comodal or hybrid format).
4 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
Due to the COVID-19 crisis, the information in this section is particularly likely to change.
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
Due to the COVID-19 crisis, the information in this section is particularly likely to change.
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
Programmes / formations proposant cette unité d'enseignement (UE)
Title of the programme
Sigle
Credits
Prerequisites
Aims
Master [120] in Data Science : Statistic
Certificat d'université : Statistique et sciences des données (15/30 crédits)
Master [120] in Agricultural Bioengineering
Master [120] in Statistic: General
Master [120] in Statistic: Biostatistics