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High-throughput data analysis for genomics

Biology Master, ENS
Year : 2 (M2)
Semester : 1 (S1)
Coordination : Stéphane Le Crom, UPMC, Gaëlle Lelandais, Denis Diderot
ECTS : 3

Presentation :

Aims : The aims of this teaching unit are to understand and handle various data format that are available from high throughput sequencing techniques in genomics.
This course allows students to better appreciate the limits and drawbacks of high throughput genomics datasets.

Themes : This course introduces the high throughput sequencing techniques and the various applications available in genomics. It focuses on the analysis of gene expression studies and covers the most important bioinformatics and statistical concepts to delineate differentially expressed genes.
Computer-training sessions make use of open source software like R programming language, MeV (MultiExperiment Viewer) and IGV (Integrative Genomics Viewer).
The course covers the following fields : data quality analysis, read mapping on a reference genome, read alignment visualization, data normalization, statistical differential analysis, functional annotation and expression network visualization.

Organization : The course is organized over one week, with classes during the morning, computer-training sessions during the afternoon and free analysis workshop around datasets.

Evaluation : Students will be evaluated based on a technical report.

Teaching team :
Stéphane Le Crom (University Pierre et Marie Curie, Paris)
Gaëlle Lelandais (University Denis Diderot, Paris)